Tracking objects in timelapse videos

Tracking objects in timelapse videos#

Tracking objects in images over time is a challenging task, in particular when these objects are splitting (such as biological cells) and merging.

In this example we demonstrate how to prompt Alice to track bright objects on dark noisy background.

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, n_parallel=3, n_iterative=3)

We use the FakeTracks dataset provided by Jean-Yves Tinevez as part example data that comes with the Trackmate Fiji Plugin.

%%alice 
There is a timelapse image dataset in "input_data/FakeTracks.tif". 
Use spot-detection for detecting the bright objects in the individual images. 
Hint: There about 1-6 objects per frame. Hence, there should be a similar number of tracks.
The use the LAP-algorithm to connect the detected dots over time. 
Viualize the tracks. Each track should have a different color. 
Make sure to remove tracks that are very short before visualizing them.
I am expecting a plot of the tracks as final result.
Result summary

9 results: Numeric: 0, String: 0, Image: 9, Dataframe: 0, Other: 0

Result tracing
Results changed from iteration to iteration as follows (final results on the right):
Process 1Image ((11653, 1042, 4))ValueErrorValueErrorImage ((731, 882, 4))
Process 2Image ((18776, 1042, 4))Image ((817, 1042, 4))
Process 3Image ((900, 1200, 4))Image ((900, 1200, 4))
Process 4ValueErrorValueErrorValueErrorImage ((817, 1049, 4))
Process 5Image ((1800, 2400, 4))Image ((1800, 2400, 4))
Process 6<Figure si... (33)Image ((900, 900, 4))
Process 7Image ((39802, 899, 4))Image ((754, 723, 4))
Process 8ValueErrorImage ((900, 1200, 4))Image ((900, 1200, 4))
Process 9iVBORw0KGg... (747724)Image ((900, 1200, 4))

Execution Output

Track visualization saved as PNG (and SVG).
/display_output/tracks.png
# --------------------------------------------------------------
# Spot detection, LAP tracking, and visualization for FakeTracks.tif
# --------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt
from skimage import io, feature, color
from scipy.optimize import linear_sum_assignment

# 1. Load image stack ------------------------------------------------
tiff_path = "input_data/FakeTracks.tif"
stack = io.imread(tiff_path)                     # (n_frames, H, W)

# 2. Detect blobs (bright spots) per frame -------------------------
blobs_per_frame = []      # list of (y, x) arrays
for idx, img in enumerate(stack):
    gray = color.rgb2gray(img) if img.ndim == 3 else img.astype(float) / 255.0
    # higher threshold to suppress noise
    blobs = feature.blob_log(
        gray,
        min_sigma=1,
        max_sigma=4,
        num_sigma=5,
        threshold=0.2,
    )                                            # tuned
    coords = blobs[:, :2] if blobs.size else np.empty((0, 2))
    blobs_per_frame.append(coords)

    # sanity‑check plot for the first frame
    if idx == 0:
        plt.figure(figsize=(5, 5))
        plt.imshow(gray, cmap="gray")
        if coords.size:
            plt.plot(coords[:, 1], coords[:, 0], "ro", markersize=4)
        plt.title("Detection sanity check (frame 0)")
        plt.axis("off")
        plt.show()

# 3. Simple LAP tracking ---------------------------------------------
max_dist = 15.0               # max allowed jump (pixels)
tracks = []                   # each track = list of [frame, y, x]

# start tracks with detections from first frame
for pt in blobs_per_frame[0]:
    tracks.append([[0, pt[0], pt[1]]])

# iterate through remaining frames
for f in range(1, len(blobs_per_frame)):
    cur_pts = blobs_per_frame[f]
    if cur_pts.size == 0:
        continue

    # If there are no existing tracks, initialise one for each detection.
    if not tracks:
        for d in range(cur_pts.shape[0]):
            tracks.append([[f, cur_pts[d, 0], cur_pts[d, 1]]])
        continue

    # Build cost matrix (Euclidean distance) between last known
    # positions of each track and the detections in the current frame.
    last_pts = np.array([trk[-1][1:] for trk in tracks])  # (n_tracks, 2)
    cost = np.linalg.norm(last_pts[:, None, :] - cur_pts[None, :, :], axis=2)
    cost[cost > max_dist] = np.inf

    # -----------------------------------------------------------------
    # Handle infeasible rows/columns before calling the Hungarian algo.
    # -----------------------------------------------------------------
    feasible_rows = np.isfinite(cost).any(axis=1)   # tracks that see at least one detection
    feasible_cols = np.isfinite(cost).any(axis=0)   # detections that are reachable

    # If nothing is feasible, start a new track for each detection.
    if not feasible_rows.any() or not feasible_cols.any():
        for d in range(cur_pts.shape[0]):
            tracks.append([[f, cur_pts[d, 0], cur_pts[d, 1]]])
        continue

    # Sub‑matrix containing only feasible rows/cols
    sub_cost = cost[feasible_rows][:, feasible_cols]

    # Solve the linear assignment problem on the reduced matrix.
    row_sub, col_sub = linear_sum_assignment(sub_cost)

    # Map back to original indices.
    row_ind = np.where(feasible_rows)[0][row_sub]
    col_ind = np.where(feasible_cols)[0][col_sub]

    assigned_tracks, assigned_dets = set(), set()
    for r, c in zip(row_ind, col_ind):
        if cost[r, c] == np.inf:
            continue
        tracks[r].append([f, cur_pts[c, 0], cur_pts[c, 1]])
        assigned_tracks.add(r)
        assigned_dets.add(c)

    # Start new tracks for detections that were not assigned.
    for d in range(cur_pts.shape[0]):
        if d not in assigned_dets:
            tracks.append([[f, cur_pts[d, 0], cur_pts[d, 1]]])

# 4. Remove very short tracks -----------------------------------------
min_length = 5                     # keep tracks spanning ≥5 frames
tracks = [trk for trk in tracks if len(trk) >= min_length]

# 5. Visualise tracks -------------------------------------------------
fig, ax = plt.subplots(figsize=(6, 5))
cmap = plt.get_cmap("tab20")
for i, trk in enumerate(tracks):
    trk_arr = np.array(trk)
    ax.plot(
        trk_arr[:, 2],
        trk_arr[:, 1],
        "-o",
        color=cmap(i % 20),
        label=f"Track {i+1}",
    )
ax.invert_yaxis()
ax.set_xlabel("X pixel")
ax.set_ylabel("Y pixel")
ax.set_title("Tracked bright objects")
if len(tracks) <= 6:
    ax.legend(loc="upper right", fontsize="small")
plt.tight_layout()

png_path = "/display_output/tracks.png"
svg_path = "/display_output/tracks.svg"
plt.savefig(png_path, dpi=150, bbox_inches="tight")
plt.savefig(svg_path, format="svg", bbox_inches="tight")
plt.show()

# 6. Final output -----------------------------------------------------
print("Track visualization saved as PNG (and SVG).")
print(png_path)   # final result: filename only
You are an expert in python programming. You are given python code, and a traceback of an error that occurred when running the python code.
Your task is to determine the new code that is required to fix the error.
Make sure to keep the code format.

The code is:
```
# --------------------------------------------------------------
# Spot detection, LAP tracking, and visualization for FakeTracks.tif
# --------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt
from skimage import io, feature, color
from scipy.optimize import linear_sum_assignment

# 1. Load image stack ------------------------------------------------
tiff_path = "input_data/FakeTracks.tif"
stack = io.imread(tiff_path)                     # (n_frames, H, W)

# 2. Detect blobs (bright spots) per frame -------------------------
blobs_per_frame = []      # list of (y, x) arrays
for idx, img in enumerate(stack):
    gray = color.rgb2gray(img) if img.ndim == 3 else img.astype(float) / 255.0
    # higher threshold to suppress noise
    blobs = feature.blob_log(
        gray,
        min_sigma=1,
        max_sigma=4,
        num_sigma=5,
        threshold=0.2,
    )                                            # tuned
    coords = blobs[:, :2] if blobs.size else np.empty((0, 2))
    blobs_per_frame.append(coords)

    # sanity‑check plot for the first frame
    if idx == 0:
        plt.figure(figsize=(5, 5))
        plt.imshow(gray, cmap="gray")
        if coords.size:
            plt.plot(coords[:, 1], coords[:, 0], "ro", markersize=4)
        plt.title("Detection sanity check (frame 0)")
        plt.axis("off")
        plt.show()

# 3. Simple LAP tracking ---------------------------------------------
max_dist = 15.0               # max allowed jump (pixels)
tracks = []                   # each track = list of [frame, y, x]

# start tracks with detections from first frame
for pt in blobs_per_frame[0]:
    tracks.append([[0, pt[0], pt[1]]])

# iterate through remaining frames
for f in range(1, len(blobs_per_frame)):
    cur_pts = blobs_per_frame[f]
    if cur_pts.size == 0:
        continue

    # If there are no existing tracks, initialise one for each detection.
    if not tracks:
        for d in range(cur_pts.shape[0]):
            tracks.append([[f, cur_pts[d, 0], cur_pts[d, 1]]])
        continue

    # Build cost matrix (Euclidean distance) between last known
    # positions of each track and the detections in the current frame.
    last_pts = np.array([trk[-1][1:] for trk in tracks])  # (n_tracks, 2)
    cost = np.linalg.norm(last_pts[:, None, :] - cur_pts[None, :, :], axis=2)
    cost[cost > max_dist] = np.inf

    # If every entry is infinite, no feasible assignment exists.
    if not np.isfinite(cost).any():
        # Start a new track for each detection.
        for d in range(cur_pts.shape[0]):
            tracks.append([[f, cur_pts[d, 0], cur_pts[d, 1]]])
        continue

    # Solve the linear assignment problem.
    row_ind, col_ind = linear_sum_assignment(cost)

    assigned_tracks, assigned_dets = set(), set()
    for r, c in zip(row_ind, col_ind):
        if cost[r, c] == np.inf:
            continue
        tracks[r].append([f, cur_pts[c, 0], cur_pts[c, 1]])
        assigned_tracks.add(r)
        assigned_dets.add(c)

    # Start new tracks for detections that were not assigned.
    for d in range(cur_pts.shape[0]):
        if d not in assigned_dets:
            tracks.append([[f, cur_pts[d, 0], cur_pts[d, 1]]])

# 4. Remove very short tracks -----------------------------------------
min_length = 5                     # keep tracks spanning ≥5 frames
tracks = [trk for trk in tracks if len(trk) >= min_length]

# 5. Visualise tracks -------------------------------------------------
fig, ax = plt.subplots(figsize=(6, 5))
cmap = plt.get_cmap("tab20")
for i, trk in enumerate(tracks):
    trk_arr = np.array(trk)
    ax.plot(
        trk_arr[:, 2],
        trk_arr[:, 1],
        "-o",
        color=cmap(i % 20),
        label=f"Track {i+1}",
    )
ax.invert_yaxis()
ax.set_xlabel("X pixel")
ax.set_ylabel("Y pixel")
ax.set_title("Tracked bright objects")
if len(tracks) <= 6:
    ax.legend(loc="upper right", fontsize="small")
plt.tight_layout()

png_path = "/display_output/tracks.png"
svg_path = "/display_output/tracks.svg"
plt.savefig(png_path, dpi=150, bbox_inches="tight")
plt.savefig(svg_path, format="svg", bbox_inches="tight")
plt.show()

# 6. Final output -----------------------------------------------------
print("Track visualization saved as PNG (and SVG).")
print(png_path)   # final result: filename only
```
The errors and stdout are:
```
An error occurred while executing the following cell:
------------------
# --------------------------------------------------------------
# Spot detection, LAP tracking, and visualization for FakeTracks.tif
# --------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt
from skimage import io, feature, color
from scipy.optimize import linear_sum_assignment

# 1. Load image stack ------------------------------------------------
tiff_path = "input_data/FakeTracks.tif"
stack = io.imread(tiff_path)                     # (n_frames, H, W)

# 2. Detect blobs (bright spots) per frame -------------------------
blobs_per_frame = []      # list of (y, x) arrays
for idx, img in enumerate(stack):
    gray = color.rgb2gray(img) if img.ndim == 3 else img.astype(float) / 255.0
    # higher threshold to suppress noise
    blobs = feature.blob_log(
        gray,
        min_sigma=1,
        max_sigma=4,
        num_sigma=5,
        threshold=0.2,
    )                                            # tuned
    coords = blobs[:, :2] if blobs.size else np.empty((0, 2))
    blobs_per_frame.append(coords)

    # sanity‑check plot for the first frame
    if idx == 0:
        plt.figure(figsize=(5, 5))
        plt.imshow(gray, cmap="gray")
        if coords.size:
            plt.plot(coords[:, 1], coords[:, 0], "ro", markersize=4)
        plt.title("Detection sanity check (frame 0)")
        plt.axis("off")
        plt.show()

# 3. Simple LAP tracking ---------------------------------------------
max_dist = 15.0               # max allowed jump (pixels)
tracks = []                   # each track = list of [frame, y, x]

# start tracks with detections from first frame
for pt in blobs_per_frame[0]:
    tracks.append([[0, pt[0], pt[1]]])

# iterate through remaining frames
for f in range(1, len(blobs_per_frame)):
    cur_pts = blobs_per_frame[f]
    if cur_pts.size == 0:
        continue

    # If there are no existing tracks, initialise one for each detection.
    if not tracks:
        for d in range(cur_pts.shape[0]):
            tracks.append([[f, cur_pts[d, 0], cur_pts[d, 1]]])
        continue

    # Build cost matrix (Euclidean distance) between last known
    # positions of each track and the detections in the current frame.
    last_pts = np.array([trk[-1][1:] for trk in tracks])  # (n_tracks, 2)
    cost = np.linalg.norm(last_pts[:, None, :] - cur_pts[None, :, :], axis=2)
    cost[cost > max_dist] = np.inf

    # If every entry is infinite, no feasible assignment exists.
    if not np.isfinite(cost).any():
        # Start a new track for each detection.
        for d in range(cur_pts.shape[0]):
            tracks.append([[f, cur_pts[d, 0], cur_pts[d, 1]]])
        continue

    # Solve the linear assignment problem.
    row_ind, col_ind = linear_sum_assignment(cost)

    assigned_tracks, assigned_dets = set(), set()
    for r, c in zip(row_ind, col_ind):
        if cost[r, c] == np.inf:
            continue
        tracks[r].append([f, cur_pts[c, 0], cur_pts[c, 1]])
        assigned_tracks.add(r)
        assigned_dets.add(c)

    # Start new tracks for detections that were not assigned.
    for d in range(cur_pts.shape[0]):
        if d not in assigned_dets:
            tracks.append([[f, cur_pts[d, 0], cur_pts[d, 1]]])

# 4. Remove very short tracks -----------------------------------------
min_length = 5                     # keep tracks spanning ≥5 frames
tracks = [trk for trk in tracks if len(trk) >= min_length]

# 5. Visualise tracks -------------------------------------------------
fig, ax = plt.subplots(figsize=(6, 5))
cmap = plt.get_cmap("tab20")
for i, trk in enumerate(tracks):
    trk_arr = np.array(trk)
    ax.plot(
        trk_arr[:, 2],
        trk_arr[:, 1],
        "-o",
        color=cmap(i % 20),
        label=f"Track {i+1}",
    )
ax.invert_yaxis()
ax.set_xlabel("X pixel")
ax.set_ylabel("Y pixel")
ax.set_title("Tracked bright objects")
if len(tracks) <= 6:
    ax.legend(loc="upper right", fontsize="small")
plt.tight_layout()

png_path = "/display_output/tracks.png"
svg_path = "/display_output/tracks.svg"
plt.savefig(png_path, dpi=150, bbox_inches="tight")
plt.savefig(svg_path, format="svg", bbox_inches="tight")
plt.show()

# 6. Final output -----------------------------------------------------
print("Track visualization saved as PNG (and SVG).")
print(png_path)   # final result: filename only
------------------


---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[1], line 72
     68             tracks.append([[f, cur_pts[d, 0], cur_pts[d, 1]]])
     69         continue
     70 
     71     # Solve the linear assignment problem.
---> 72     row_ind, col_ind = linear_sum_assignment(cost)
     73 
     74     assigned_tracks, assigned_dets = set(), set()
     75     for r, c in zip(row_ind, col_ind):

ValueError: cost matrix is infeasible


```

```

```
Return the new code and nothing else.

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: 5.14s
  • Execution Time: 5.24s
  • Total Time: 176.74s
  • Files:
    • /display_output/notebook_executed.ipynb
    • /display_output/tracks.png
    • /display_output/tracks.svg
LLM backend
TaskFunctionModel
Generate codeprompt_scadsai_llmopenai/gpt-oss-120b
Fix codeprompt_scadsai_llmopenai/gpt-oss-120b
Determine dependenciesprompt_scadsai_llmopenai/gpt-oss-120b
Generate code feedbackprompt_scadsai_llmgoogle/gemma-4-31B-it
Summarize codeprompt_scadsai_llmopenai/gpt-oss-120b
Notebook conversionprompt_scadsai_llmopenai/gpt-oss-120b

Execution Output

Tracks visualized and saved.
/display_output/tracks.png
# ------------------------------------------------------------
# Spot detection + LAP tracking with intermediate detection plot
# ------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt
from skimage import io, feature
from scipy.optimize import linear_sum_assignment
from pathlib import Path

# ---------- 1. Load the timelapse stack ----------
tif_path = Path("input_data/FakeTracks.tif")
stack = io.imread(tif_path)                     # (n_frames, H, W)

# ---------- 2. Detect bright spots in every frame ----------
blobs_per_frame = []                            # list of (y,x) per frame
for frame in stack:
    blobs = feature.blob_log(frame,
                             min_sigma=1,
                             max_sigma=5,
                             num_sigma=5,
                             threshold=0.02)
    blobs_per_frame.append(blobs[:, :2])        # keep (y, x)

# ---------- 2a. Sanity check: show detections on first frame ----------
plt.figure(figsize=(5, 5))
plt.imshow(stack[0], cmap="gray")
first_blobs = blobs_per_frame[0]
if first_blobs.size:
    plt.scatter(first_blobs[:, 1], first_blobs[:, 0],
                facecolors='none', edgecolors='r', s=30)
plt.title("Detection sanity check (frame 0)")
plt.axis('off')
plt.show()

# ---------- 3. Simple LAP linking ----------
max_dist = 15          # max jump (pixels)
min_track_len = 3      # drop short tracks

tracks = []            # completed tracks
active = []            # currently growing tracks (dicts)

for t, detections in enumerate(blobs_per_frame):
    detections = np.asarray(detections)
    n_det = len(detections)

    if not active:
        for d in detections:
            active.append(dict(frames=[t], ys=[d[0]], xs=[d[1]]))
        continue

    last_pos = np.array([[tr['ys'][-1], tr['xs'][-1]] for tr in active])

    if n_det == 0:
        tracks.extend(active)
        active = []
        continue

    cost = np.linalg.norm(last_pos[:, None, :] - detections[None, :, :], axis=2)
    cost[cost > max_dist] = 1e6
    row_ind, col_ind = linear_sum_assignment(cost)

    assigned_det, assigned_trk = set(), set()
    for r, c in zip(row_ind, col_ind):
        if cost[r, c] > max_dist:
            continue
        trk = active[r]
        det = detections[c]
        trk['frames'].append(t)
        trk['ys'].append(det[0])
        trk['xs'].append(det[1])
        assigned_det.add(c)
        assigned_trk.add(r)

    # finish unmatched active tracks
    for i, trk in enumerate(active):
        if i not in assigned_trk:
            tracks.append(trk)
    active = [active[i] for i in assigned_trk]

    # start new tracks for unmatched detections
    for i, det in enumerate(detections):
        if i not in assigned_det:
            active.append(dict(frames=[t], ys=[det[0]], xs=[det[1]]))

# add any remaining active tracks
tracks.extend(active)

# ---------- 4. Remove very short tracks ----------
filtered_tracks = [tr for tr in tracks if len(tr['frames']) >= min_track_len]

# ---------- 5. Visualise the tracks (no legend) ----------
plt.figure(figsize=(8, 6))
cmap = plt.get_cmap("tab20")
for idx, tr in enumerate(filtered_tracks):
    color = cmap(idx % cmap.N)
    plt.plot(tr['xs'], tr['ys'],
             marker='o', markersize=4,
             linewidth=1.5, color=color)

plt.gca().invert_yaxis()
plt.title("Tracked bright objects")
plt.xlabel("X pixel")
plt.ylabel("Y pixel")
plt.axis('equal')

out_dir = Path("/display_output")
png_path = out_dir / "tracks.png"
svg_path = out_dir / "tracks.svg"
plt.savefig(png_path, dpi=150, bbox_inches='tight')
plt.savefig(svg_path, format='svg', bbox_inches='tight')
plt.show()

# ---------- 6. Final output ----------
print("Tracks visualized and saved.")      # second‑last print (description)
print(png_path)                           # last print: final result (PNG filename)
You are an expert in python programming. You have a list of framework constraints which you MUST follow.
Your task is to generate a fully functional code snippet that will be used to fulfill the prompt.
Assume your code will be executed in a Jupyter notebook cell.

# Framework constraints
* You may use the following libraries, but only if necessary: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
* When saving files, use the following folder: "/display_output/". Do not create this folder. It exists already.
* pip install is STRICTLY PROHIBITED. You can only use the libraries mentioned above.
* Statistics: When applying statisticals test, ENSURE that pre-conditions for the tests are checked before the tests are performed.
* Final result output (print or display calls): 
  * The second-last print or display call should be a description of the result (e.g. the measurment and a physical unit if relevant).
  * The last print or display call should be the final result ONLY.
  * If the task is to generate a count, ratio or measurements, print the final result using a separate `print` call. 
  * If the task is to answer a yes/no question, print "Yes" or "No" using a separate `print` call. Do not create any JSON for this.
  * If the task is to generate a plot, display the plot.
  * Also plot intermediate results if possible.
* Final result output (file writing):
  * If the task is to generate a text or a string, write the text or string to "/display_output/final_result.txt".
  * If the task is to generate a table, write the table to "/display_output/final_result.csv".
  * If the final result is a plot, display this plot and afterwards, save it in the folder /display_output as .png and as .svg file and print the filename of the .png in the final output of the program.
  * If the task is to generate a number, list, array or dictionary, write the result to "/display_output/final_result.json".
    In that case, do not add additional data structures. Simply json.dump the result to the file. E.g. if the result is x=2, then just do `json.dump(x, fp)`.
  * If the final result was saved as file, make sure to print the filename in the final output of the program.  
* Keep the code short and concise.



Given some task, code to fulfill the task, and detailed feedback, propose new code that incorporates the feedback.
Make sure to keep the code format.

# Task
                
There is a timelapse image dataset in "input_data/FakeTracks.tif". 
Use spot-detection for detecting the bright objects in the individual images. 
Hint: There about 1-6 objects per frame. Hence, there should be a similar number of tracks.
The use the LAP-algorithm to connect the detected dots over time. 
Viualize the tracks. Each track should have a different color. 
Make sure to remove tracks that are very short before visualizing them.
I am expecting a plot of the tracks as final result.

                
# Code

```
# ------------------------------------------------------------
# Spot detection + simple LAP tracking, then plot the tracks
# ------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt
from skimage import io, feature
from scipy.optimize import linear_sum_assignment
from pathlib import Path

# ---------- 1. Load the timelapse stack ----------
tif_path = Path("input_data/FakeTracks.tif")
stack = io.imread(tif_path)                     # shape: (n_frames, height, width)

# ---------- 2. Detect bright spots (blobs) in every frame ----------
# Parameters are chosen for small bright objects
blobs_per_frame = []                            # list of (y,x) arrays per frame
for frame in stack:
    # log returns (y, x, sigma) – we keep only y & x
    blobs = feature.blob_log(frame,
                             min_sigma=1,
                             max_sigma=5,
                             num_sigma=5,
                             threshold=0.02)   # adjust if needed
    blobs_per_frame.append(blobs[:, :2])        # shape (n_blob, 2)

# ---------- 3. Simple LAP linking over time ----------
max_dist = 15          # maximum allowed jump between frames (pixels)
min_track_len = 3      # drop tracks shorter than this

tracks = []            # completed tracks: list of [(frame,y,x), ...]
active = []            # currently growing tracks: list of dicts

for t, detections in enumerate(blobs_per_frame):
    # Convert detections to (y,x) array
    detections = np.asarray(detections)
    n_det = len(detections)

    # If no active tracks, start a new one for each detection
    if not active:
        for d in detections:
            active.append(dict(frames=[t], ys=[d[0]], xs=[d[1]]))
        continue

    # Build cost matrix between active tracks' last positions and current detections
    last_pos = np.array([[tr['ys'][-1], tr['xs'][-1]] for tr in active])
    if n_det == 0:
        # No detections → all active become finished
        tracks.extend(active)
        active = []
        continue

    cost = np.linalg.norm(last_pos[:, None, :] - detections[None, :, :], axis=2)
    cost[cost > max_dist] = 1e6                     # forbid large jumps

    # Solve assignment
    row_ind, col_ind = linear_sum_assignment(cost)

    # Keep track of which detections got assigned
    assigned_det = set()
    assigned_trk = set()

    for r, c in zip(row_ind, col_ind):
        if cost[r, c] > max_dist:   # ignore assignments that are too far
            continue
        trk = active[r]
        det = detections[c]
        trk['frames'].append(t)
        trk['ys'].append(det[0])
        trk['xs'].append(det[1])
        assigned_det.add(c)
        assigned_trk.add(r)

    # Unmatched active tracks become finished
    for i, trk in enumerate(active):
        if i not in assigned_trk:
            tracks.append(trk)
    # Remove them from active list
    active = [active[i] for i in assigned_trk]

    # Unmatched detections start new tracks
    for i, det in enumerate(detections):
        if i not in assigned_det:
            active.append(dict(frames=[t], ys=[det[0]], xs=[det[1]]))

# Add remaining active tracks at the end
tracks.extend(active)

# ---------- 4. Filter out very short tracks ----------
filtered_tracks = [tr for tr in tracks if len(tr['frames']) >= min_track_len]

# ---------- 5. Visualise the tracks ----------
plt.figure(figsize=(8, 6))
cmap = plt.get_cmap("tab20")
n_tracks = len(filtered_tracks)

for idx, tr in enumerate(filtered_tracks):
    color = cmap(idx % cmap.N)
    plt.plot(tr['xs'], tr['ys'],
             marker='o', markersize=4,
             linewidth=1.5, color=color,
             label=f'Track {idx+1}')

plt.gca().invert_yaxis()          # image coordinates: origin top‑left
plt.title("Tracked bright objects")
plt.xlabel("X pixel")
plt.ylabel("Y pixel")
plt.axis('equal')
plt.legend(loc='upper right', fontsize='small', ncol=2)

# Save figure
out_dir = Path("/display_output")
png_file = out_dir / "tracks.png"
svg_file = out_dir / "tracks.svg"
plt.savefig(png_file, dpi=150, bbox_inches='tight')
plt.savefig(svg_file, format='svg', bbox_inches='tight')
plt.show()

# ---------- 6. Final outputs ----------
print("Tracks visualized (each track a different colour).")
print(png_file)   # final result: filename of the saved PNG
```

# Feedback

### Feedback

**Data Analysis & Image Analysis**
The code performs spot detection and tracking without visualizing the intermediate results. It is critical to display at least one frame with the detected blobs to ensure the `threshold` and `sigma` parameters are correct. Without this, it is impossible to know if the tracking is failing due to the LAP algorithm or due to poor detection (under/over-segmentation).

**Code Quality & Complexity**
The track management logic using a list of dictionaries and manual indexing is overly complex and prone to errors. Using a simple list of coordinates or a NumPy array per track is more efficient. Additionally, the legend in the plot is overwhelming because it tries to label every single track (as seen in the result image), which obscures the plot.

**Final Result**
The final output should strictly be the filename(s) of the resulting plot as per the instructions.

#### Suggested Improvements

**1. Add Sanity Check for Detection**
```python
# Add after detecting blobs in the first frame
plt.figure()
plt.imshow(stack[0], cmap='gray')
first_blobs = blobs_per_frame[0]
if len(first_blobs) > 0:
    plt.scatter(first_blobs[:, 1], first_blobs[:, 0], color='red', s=10)
plt.title("Detection Sanity Check (Frame 0)")
plt.show()
```

**2. Simplify Track Management and Fix Legend**
```python
# Old code:
# plt.plot(tr['xs'], tr['ys'], ..., label=f'Track {idx+1}')
# plt.legend(loc='upper right', fontsize='small', ncol=2)

# New code:
for idx, tr in enumerate(filtered_tracks):
    color = cmap(idx % cmap.N)
    plt.plot(tr['xs'], tr['ys'], marker='o', markersize=4, 
             linewidth=1.5, color=color) 
# Remove plt.legend() entirely or only label a few tracks, 
# as 100+ labels make the plot unreadable.
```

**3. Clean up Final Output**
```python
# Old code:
# print("Tracks visualized (each track a different colour).")
# print(png_file)

# New code:
print(png_file)
print(svg_file)
```

**Summary of modifications:**
* Added a visualization step for blob detection to ensure parameters are correct.
* Removed the legend from the final plot to prevent it from covering the data.
* Streamlined the final print statements to output only the filenames.

# Your task
Provide the updated code to incorporate the feedback. Also make sure the original task will be fulfilled. Skip all explanations.

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: 11.64s
  • Execution Time: 11.73s
  • Total Time: 112.51s
  • Files:
    • /display_output/notebook_executed.ipynb
    • /display_output/tracks.png
    • /display_output/tracks.svg
LLM backend
TaskFunctionModel
Generate codeprompt_scadsai_llmopenai/gpt-oss-120b
Fix codeprompt_scadsai_llmopenai/gpt-oss-120b
Determine dependenciesprompt_scadsai_llmopenai/gpt-oss-120b
Generate code feedbackprompt_scadsai_llmgoogle/gemma-4-31B-it
Summarize codeprompt_scadsai_llmopenai/gpt-oss-120b
Notebook conversionprompt_scadsai_llmopenai/gpt-oss-120b

Execution Output

Detecting blobs:   0%|          | 0/50 [00:00<?, ?it/s]
Detecting blobs:  30%|███       | 15/50 [00:00<00:00, 148.49it/s]
Detecting blobs:  74%|███████▍  | 37/50 [00:00<00:00, 187.21it/s]
Detecting blobs: 100%|██████████| 50/50 [00:00<00:00, 189.21it/s]
Linking frames:   0%|          | 0/49 [00:00<?, ?it/s]
Linking frames: 100%|██████████| 49/49 [00:00<00:00, 63846.19it/s]
Plot of the detected tracks (each colour corresponds to a separate track).
/display_output/tracks.png
# Spot‑detection + LAP tracking for a TIFF timelapse (improved)
# -----------------------------------------------------------
# 1. Load the image stack
# 2. Detect bright spots (blob_log) with higher threshold & sanity checks
# 3. Visualise detections on the first few frames
# 4. Link detections across frames (LAP / Hungarian)
# 5. Discard very short tracks
# 6. Plot remaining tracks (one colour per track, legend only if few tracks)
# 7. Save figure (png & svg) and print the png filename as final result

import numpy as np
import matplotlib.pyplot as plt
from skimage import io, feature
from scipy.spatial import distance
from scipy.optimize import linear_sum_assignment
from tqdm import tqdm

# --------------------------------------------------
# 1. Load image stack
tiff_path = "input_data/FakeTracks.tif"
stack = io.imread(tiff_path)               # (n_frames, H, W)
n_frames = stack.shape[0]

# --------------------------------------------------
# 2. Spot detection (higher threshold, sanity checks)
blobs_per_frame = []                       # list of (y, x) arrays per frame
for i in tqdm(range(n_frames), desc="Detecting blobs"):
    image = stack[i]
    blobs = feature.blob_log(
        image,
        min_sigma=2,
        max_sigma=5,
        num_sigma=5,
        threshold=0.1,          # increased to suppress noise
    )
    # sanity check
    if len(blobs) == 0 or len(blobs) > 20:
        print(f"Warning: Frame {i} detected {len(blobs)} blobs.")
    blobs_per_frame.append(blobs[:, :2])   # keep (y, x)

# --------------------------------------------------
# 3. Visualise detections on the first 3 frames
max_vis = min(3, n_frames)
fig, axes = plt.subplots(1, max_vis, figsize=(4 * max_vis, 4))
if max_vis == 1:
    axes = [axes]
for idx, ax in enumerate(axes):
    ax.imshow(stack[idx], cmap='gray')
    if len(blobs_per_frame[idx]) > 0:
        y, x = blobs_per_frame[idx][:, 0], blobs_per_frame[idx][:, 1]
        ax.scatter(x, y, s=30, facecolors='none', edgecolors='r')
    ax.set_title(f'Frame {idx}')
    ax.axis('off')
plt.tight_layout()
plt.show()

# --------------------------------------------------
# 4. Link detections with LAP (frame‑to‑frame Hungarian)
max_distance = 10.0          # max allowed jump (pixels)
next_track_id = 0
tracks = {}                  # track_id -> list of (frame, y, x)

# initialise with first frame detections
for det in blobs_per_frame[0]:
    tracks[next_track_id] = [(0, det[0], det[1])]
    next_track_id += 1

# propagate through remaining frames
for t in tqdm(range(1, n_frames), desc="Linking frames"):
    # active tracks ending at previous frame
    prev_dets = np.array([
        [tracks[tid][-1][1], tracks[tid][-1][2]]
        for tid in tracks
        if tracks[tid][-1][0] == t - 1
    ])
    prev_ids = [tid for tid in tracks if tracks[tid][-1][0] == t - 1]

    curr_dets = blobs_per_frame[t]
    if prev_dets.size == 0:
        # start new tracks for all detections
        for det in curr_dets:
            tracks[next_track_id] = [(t, det[0], det[1])]
            next_track_id += 1
        continue

    cost = distance.cdist(prev_dets, curr_dets, metric='euclidean')
    cost[cost > max_distance] = 1e5   # penalise impossible jumps

    row_ind, col_ind = linear_sum_assignment(cost)

    assigned_curr = set()
    for r, c in zip(row_ind, col_ind):
        if cost[r, c] >= 1e5:
            continue
        tid = prev_ids[r]
        det = curr_dets[c]
        tracks[tid].append((t, det[0], det[1]))
        assigned_curr.add(c)

    # remaining detections start new tracks
    for idx, det in enumerate(curr_dets):
        if idx not in assigned_curr:
            tracks[next_track_id] = [(t, det[0], det[1])]
            next_track_id += 1

# --------------------------------------------------
# 5. Remove very short tracks
min_track_length = 3   # frames
filtered_tracks = {
    tid: pts for tid, pts in tracks.items() if len(pts) >= min_track_length
}

# --------------------------------------------------
# 6. Visualise tracks
plt.figure(figsize=(8, 6))
cmap = plt.get_cmap('tab20')
for i, (tid, pts) in enumerate(filtered_tracks.items()):
    pts_arr = np.array(pts)          # (N, 3) → frame, y, x
    plt.plot(pts_arr[:, 2], pts_arr[:, 1],
             '-o', color=cmap(i % 20), markersize=4, linewidth=1.5)

plt.gca().invert_yaxis()
plt.title('Detected Tracks')
plt.xlabel('X pixel')
plt.ylabel('Y pixel')
# Add legend only if number of tracks reasonable
if len(filtered_tracks) <= 10:
    handles = [plt.Line2D([0], [0], color=cmap(i % 20), lw=2) 
               for i in range(len(filtered_tracks))]
    labels = [f'Track {tid}' for tid in filtered_tracks.keys()]
    plt.legend(handles, labels, fontsize='small', loc='upper right')
plt.tight_layout()

# --------------------------------------------------
# 7. Save figure and final output
out_png = "/display_output/tracks.png"
out_svg = "/display_output/tracks.svg"
plt.savefig(out_png, dpi=150)
plt.savefig(out_svg)
plt.show()

print("Plot of the detected tracks (each colour corresponds to a separate track).")
print(out_png)
You are an expert in python programming. You have a list of framework constraints which you MUST follow.
Your task is to generate a fully functional code snippet that will be used to fulfill the prompt.
Assume your code will be executed in a Jupyter notebook cell.

# Framework constraints
* You may use the following libraries, but only if necessary: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
* When saving files, use the following folder: "/display_output/". Do not create this folder. It exists already.
* pip install is STRICTLY PROHIBITED. You can only use the libraries mentioned above.
* Statistics: When applying statisticals test, ENSURE that pre-conditions for the tests are checked before the tests are performed.
* Final result output (print or display calls): 
  * The second-last print or display call should be a description of the result (e.g. the measurment and a physical unit if relevant).
  * The last print or display call should be the final result ONLY.
  * If the task is to generate a count, ratio or measurements, print the final result using a separate `print` call. 
  * If the task is to answer a yes/no question, print "Yes" or "No" using a separate `print` call. Do not create any JSON for this.
  * If the task is to generate a plot, display the plot.
  * Also plot intermediate results if possible.
* Final result output (file writing):
  * If the task is to generate a text or a string, write the text or string to "/display_output/final_result.txt".
  * If the task is to generate a table, write the table to "/display_output/final_result.csv".
  * If the final result is a plot, display this plot and afterwards, save it in the folder /display_output as .png and as .svg file and print the filename of the .png in the final output of the program.
  * If the task is to generate a number, list, array or dictionary, write the result to "/display_output/final_result.json".
    In that case, do not add additional data structures. Simply json.dump the result to the file. E.g. if the result is x=2, then just do `json.dump(x, fp)`.
  * If the final result was saved as file, make sure to print the filename in the final output of the program.  
* Keep the code short and concise.



Given some task, code to fulfill the task, and detailed feedback, propose new code that incorporates the feedback.
Make sure to keep the code format.

# Task
                
There is a timelapse image dataset in "input_data/FakeTracks.tif". 
Use spot-detection for detecting the bright objects in the individual images. 
Hint: There about 1-6 objects per frame. Hence, there should be a similar number of tracks.
The use the LAP-algorithm to connect the detected dots over time. 
Viualize the tracks. Each track should have a different color. 
Make sure to remove tracks that are very short before visualizing them.
I am expecting a plot of the tracks as final result.

                
# Code

```
# Spot‑detection + LAP tracking for a TIFF timelapse
# -------------------------------------------------
# 1. Load the image stack
# 2. Detect bright spots in each frame (blob_log)
# 3. Link detections across frames with the Hungarian (LAP) algorithm
# 4. Discard very short tracks
# 5. Plot each remaining track in a unique colour
# 6. Save the figure (png & svg) and print the png filename as the final result

import numpy as np
import matplotlib.pyplot as plt
from skimage import io, feature
from scipy.spatial import distance
from scipy.optimize import linear_sum_assignment
from tqdm import tqdm
import os

# ------------------------------------------------------------------
# 1. Load image stack
tiff_path = "input_data/FakeTracks.tif"
stack = io.imread(tiff_path)               # shape: (n_frames, H, W)
n_frames = stack.shape[0]

# ------------------------------------------------------------------
# 2. Spot detection (Laplacian‑of‑Gaussian)
#    Adjust parameters if necessary for the specific data.
blobs_per_frame = []                       # list of (y, x) arrays

for i in tqdm(range(n_frames), desc="Detecting blobs"):
    image = stack[i]
    # blob_log returns (y, x, sigma); we keep only y and x
    blobs = feature.blob_log(
        image,
        min_sigma=2,
        max_sigma=5,
        num_sigma=5,
        threshold=0.02,          # fairly low – adapt if too many/few detections
    )
    blobs_per_frame.append(blobs[:, :2])   # (y, x)

# ------------------------------------------------------------------
# 3. Link detections with a simple LAP (Hungarian) approach
max_distance = 10.0          # max allowed jump between successive frames (pixels)
next_track_id = 0
tracks = {}                  # track_id -> list of (frame, y, x)

# initialise tracks with detections from the first frame
for det in blobs_per_frame[0]:
    tracks[next_track_id] = [(0, det[0], det[1])]
    next_track_id += 1

# propagate through the remaining frames
for t in tqdm(range(1, n_frames), desc="Linking frames"):
    prev_dets = np.array([ [tracks[tid][-1][1], tracks[tid][-1][2]] 
                           for tid in sorted(tracks) 
                           if tracks[tid][-1][0] == t-1 ])
    prev_ids  = [tid for tid in sorted(tracks) 
                 if tracks[tid][-1][0] == t-1]

    curr_dets = blobs_per_frame[t]
    if prev_dets.size == 0:                     # no active tracks – start new ones
        for det in curr_dets:
            tracks[next_track_id] = [(t, det[0], det[1])]
            next_track_id += 1
        continue

    # distance matrix
    cost = distance.cdist(prev_dets, curr_dets, metric='euclidean')
    # impose a large penalty for distances beyond the allowed jump
    cost[cost > max_distance] = 1e5

    # Hungarian assignment
    row_ind, col_ind = linear_sum_assignment(cost)

    # keep track of which current detections got assigned
    assigned_curr = set()
    # update matched tracks
    for r, c in zip(row_ind, col_ind):
        if cost[r, c] >= 1e5:               # too far → treat as no match
            continue
        tid = prev_ids[r]
        det = curr_dets[c]
        tracks[tid].append((t, det[0], det[1]))
        assigned_curr.add(c)

    # any unassigned current detections start new tracks
    for idx, det in enumerate(curr_dets):
        if idx not in assigned_curr:
            tracks[next_track_id] = [(t, det[0], det[1])]
            next_track_id += 1

# ------------------------------------------------------------------
# 4. Remove very short tracks
min_track_length = 3          # frames
filtered_tracks = {tid: ptlist for tid, ptlist in tracks.items()
                   if len(ptlist) >= min_track_length}

# ------------------------------------------------------------------
# 5. Visualise tracks – each track a distinct colour
plt.figure(figsize=(8, 6))
cmap = plt.get_cmap('tab20')
for i, (tid, pts) in enumerate(filtered_tracks.items()):
    pts = np.array(pts)                 # (N, 3) → frame, y, x
    plt.plot(pts[:, 2], pts[:, 1],
             '-o', color=cmap(i % 20),
             label=f'Track {tid}')

plt.gca().invert_yaxis()                # image‑style coordinates
plt.title('Detected Tracks (different colours = different tracks)')
plt.xlabel('X pixel')
plt.ylabel('Y pixel')
plt.legend(loc='upper right', fontsize='small', ncol=2)

# ------------------------------------------------------------------
# 6. Save figure and print the result filename
out_png = "/display_output/tracks.png"
out_svg = "/display_output/tracks.svg"
plt.tight_layout()
plt.savefig(out_png, dpi=150)
plt.savefig(out_svg)
plt.show()

# ------------------------------------------------------------------
# Final output (as required by the framework)
print("Plot of the detected tracks (each colour corresponds to a separate track).")
print(out_png)
```

# Feedback

The current code has a significant failure in the spot-detection and filtering stages, resulting in an extreme over-segmentation (too many tracks) and a plot that is unusable due to an oversized legend.

### Image Analysis & Data Analysis
The final plot shows a "cloud" of short, random tracks rather than the 1-6 expected tracks. This indicates that the `threshold` in `blob_log` is too low, picking up noise as spots. Furthermore, there are no intermediate visualizations to verify if the spots are correctly detected before linking.

### Code Complexity & Visualization
The legend is attempting to display hundreds of tracks, which crashes the layout and obscures the data. Since the task specifies 1-6 tracks, the presence of hundreds of tracks is a red flag that should have been caught by a sanity check.

### Improvements

**1. Improve Spot Detection & Sanity Checks**
Add a visualization of the detected spots on a few frames and increase the threshold to avoid noise.

**2. Fix Legend and Visualization**
Remove the labels from the `plt.plot` call if there are too many tracks, or better yet, fix the detection so the legend remains manageable.

**3. Refine the LAP logic**
The current logic is a simple frame-to-frame match. While acceptable for this task, ensuring the threshold is high enough is the priority.

#### Old Code (Spot Detection & Plotting)
```python
    blobs = feature.blob_log(
        image,
        min_sigma=2,
        max_sigma=5,
        num_sigma=5,
        threshold=0.02,          # fairly low – adapt if too many/few detections
    )
...
    plt.plot(pts[:, 2], pts[:, 1],
             '-o', color=cmap(i % 20),
             label=f'Track {tid}')
```

#### New Code (Improved Spot Detection & Plotting)
```python
# 2. Spot detection
# Use a significantly higher threshold to avoid noise
# Sanity check: print number of blobs detected per frame
for i in tqdm(range(n_frames), desc="Detecting blobs"):
    image = stack[i]
    blobs = feature.blob_log(
        image,
        min_sigma=2,
        max_sigma=5,
        num_sigma=5,
        threshold=0.1, # Increased to filter noise
    )
    if len(blobs) == 0 or len(blobs) > 20:
        print(f"Warning: Frame {i} detected {len(blobs)} blobs.")
    blobs_per_frame.append(blobs[:, :2])

# ... (Linking logic remains similar) ...

# 5. Visualise tracks
plt.figure(figsize=(8, 6))
cmap = plt.get_cmap('tab20')
for i, (tid, pts) in enumerate(filtered_tracks.items()):
    pts = np.array(pts)
    plt.plot(pts[:, 2], pts[:, 1], '-o', color=cmap(i % 20), markersize=3)

plt.gca().invert_yaxis()
plt.title('Detected Tracks')
plt.xlabel('X pixel')
plt.ylabel('Y pixel')
# Remove label=f'Track {tid}' from plot call to avoid giant legend
# Only add legend if number of tracks is reasonable
if len(filtered_tracks) < 20:
    plt.legend([plt.Line2D([0], [0], color=cmap(i % 20)) for i in range(len(filtered_tracks))], 
               [f'Track {tid}' for tid in filtered_tracks.keys()], fontsize='small')
```

### Summary of modifications:
*   **Threshold adjustment**: Increased `threshold` in `blob_log` to prevent noise from being detected as spots.
*   **Sanity Check**: Added a print statement to alert the user if the number of detections per frame is unrealistic.
*   **Visualization**: Removed the individual track labels from the plot loop to prevent the legend from overlapping the entire figure.
*   **Image Analysis**: Identified that the over-segmentation was the primary cause of the failure.

# Your task
Provide the updated code to incorporate the feedback. Also make sure the original task will be fulfilled. Skip all explanations.

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: 5.04s
  • Execution Time: 5.14s
  • Total Time: 115.20s
  • Files:
    • /display_output/notebook_executed.ipynb
    • /display_output/tracks.png
    • /display_output/tracks.svg
LLM backend
TaskFunctionModel
Generate  codeprompt_scadsai_llmopenai/gpt-oss-120b
Fix codeprompt_scadsai_llmopenai/gpt-oss-120b
Determine dependenciesprompt_scadsai_llmopenai/gpt-oss-120b
Generate code  feedbackprompt_scadsai_llmgoogle/gemma-4-31B-it
Summarize codeprompt_scadsai_llmopenai/gpt-oss-120b
Notebook conversionprompt_scadsai_llmopenai/gpt-oss-120b













    


    

    
Execution Output
Detecting spots:   0%|          | 0/50 [00:00<?, ?it/s]
Detecting spots:  24%|██▍       | 12/50 [00:00<00:00, 116.75it/s]
Detecting spots:  48%|████▊     | 24/50 [00:00<00:00, 115.69it/s]
Detecting spots:  72%|███████▏  | 36/50 [00:00<00:00, 115.68it/s]
Detecting spots:  96%|█████████▌| 48/50 [00:00<00:00, 113.65it/s]
Detecting spots: 100%|██████████| 50/50 [00:00<00:00, 113.10it/s]
/tmp/ipykernel_14/331055303.py:46: UserWarning: Some frames contain more than 6 spots; check parameters.
  warnings.warn("Some frames contain more than 6 spots; check parameters.")
/tmp/ipykernel_14/331055303.py:112: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
  plt.legend(loc='best', fontsize='small', ncol=2)
/display_output/tracks.png
/display_output/tracks.svg
# --------------------------------------------------------------
# Spot detection + LAP tracking for a TIFF timelapse (updated)
# --------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt
from skimage import io, feature, img_as_float
from scipy.optimize import linear_sum_assignment
from tqdm import tqdm
import warnings

# ---- 1. Load the image stack ------------------------------------------------
tif_path = "input_data/FakeTracks.tif"
stack = io.imread(tif_path)          # (n_frames, height, width)
n_frames = stack.shape[0]

# ---- 2. Spot detection (simple global normalisation) -----------------------
def detect_spots(image, min_sigma=2, max_sigma=5, num_sigma=10, thresh=0.03):
    """Return (y, x) coordinates of blobs (LoG) after simple normalisation."""
    img = img_as_float(image)
    img = (img - img.min()) / (img.max() - img.min())   # global 0‑1 scaling
    blobs = feature.blob_log(img,
                             min_sigma=min_sigma,
                             max_sigma=max_sigma,
                             num_sigma=num_sigma,
                             threshold=thresh)
    return blobs[:, :2]   # (y, x)

# ---- 2a. Sanity‑check visualisation of detections on first frame ----------
sample_frame = img_as_float(stack[0])
sample_spots = detect_spots(sample_frame)
plt.figure(figsize=(5, 4))
plt.imshow(sample_frame, cmap='gray')
plt.plot(sample_spots[:, 1], sample_spots[:, 0], 'ro', markersize=5)
plt.title("Detection sanity check (frame 0)")
plt.axis('off')
plt.show()

# ---- 3. Detect spots in all frames -----------------------------------------
detections = [detect_spots(frame) for frame in tqdm(stack, desc="Detecting spots")]

# sanity check: expected 1‑6 spots per frame
counts = np.array([len(d) for d in detections])
if np.any(counts == 0):
    warnings.warn("Some frames contain no detected spots.")
if np.any(counts > 6):
    warnings.warn("Some frames contain more than 6 spots; check parameters.")

# ---- 4. Simple LAP tracking -------------------------------------------------
max_link_distance = 15.0   # pixels
tracks = []                # each track: {'frames': [], 'positions': []}
active = []                # indices of currently active tracks

for f, spots in enumerate(detections):
    spots = np.asarray(spots)               # (n_spots, 2)

    if f == 0:                              # initialise tracks
        for s in spots:
            tracks.append(dict(frames=[0], positions=[s]))
            active.append(len(tracks) - 1)
        continue

    if len(active) == 0 or len(spots) == 0:   # no linking possible
        for s in spots:
            tracks.append(dict(frames=[f], positions=[s]))
            active.append(len(tracks) - 1)
        continue

    prev_pos = np.array([tracks[i]['positions'][-1] for i in active])   # (n_active, 2)
    cost = np.linalg.norm(prev_pos[:, None, :] - spots[None, :, :], axis=2)  # (n_active, n_spots)

    # assignment (Hungarian)
    row_ind, col_ind = linear_sum_assignment(cost)

    matched_prev = set()
    matched_spot = set()
    for r, c in zip(row_ind, col_ind):
        if cost[r, c] <= max_link_distance:
            track_idx = active[r]
            tracks[track_idx]['frames'].append(f)
            tracks[track_idx]['positions'].append(spots[c])
            matched_prev.add(r)
            matched_spot.add(c)

    # tracks not matched become inactive
    new_active = [active[i] for i in range(len(active)) if i not in matched_prev]

    # unmatched detections start new tracks
    for s_idx, s in enumerate(spots):
        if s_idx not in matched_spot:
            tracks.append(dict(frames=[f], positions=[s]))
            new_active.append(len(tracks) - 1)

    active = new_active

# ---- 5. Remove very short tracks -------------------------------------------
min_track_length = 5   # frames
filtered_tracks = [t for t in tracks if len(t['frames']) >= min_track_length]

# ---- 6. Visualise tracks ---------------------------------------------------
plt.figure(figsize=(8, 6))
cmap = plt.get_cmap("tab20")
for i, tr in enumerate(filtered_tracks):
    pos = np.array(tr['positions'])
    plt.plot(pos[:, 1], pos[:, 0], '-o',
             color=cmap(i % cmap.N), label=f"track {i+1}")

plt.gca().invert_yaxis()
plt.title("Detected object tracks")
plt.xlabel("X pixel")
plt.ylabel("Y pixel")
plt.axis('equal')
plt.legend(loc='best', fontsize='small', ncol=2)

out_png = "/display_output/tracks.png"
out_svg = "/display_output/tracks.svg"
plt.savefig(out_png, dpi=150, bbox_inches='tight')
plt.savefig(out_svg, format='svg', bbox_inches='tight')
plt.show()

# ---- 7. Final output --------------------------------------------------------
print(out_png)
print(out_svg)
You are an expert in python programming. You have a list of framework constraints which you MUST follow.
Your task is to generate a fully functional code snippet that will be used to fulfill the prompt.
Assume your code will be executed in a Jupyter notebook cell.

# Framework constraints
* You may use the following libraries, but only if necessary: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
* When saving files, use the following folder: "/display_output/". Do not create this folder. It exists already.
* pip install is STRICTLY PROHIBITED. You can only use the libraries mentioned above.
* Statistics: When applying statisticals test, ENSURE that pre-conditions for the tests are checked before the tests are performed.
* Final result output (print or display calls): 
  * The second-last print or display call should be a description of the result (e.g. the measurment and a physical unit if relevant).
  * The last print or display call should be the final result ONLY.
  * If the task is to generate a count, ratio or measurements, print the final result using a separate `print` call. 
  * If the task is to answer a yes/no question, print "Yes" or "No" using a separate `print` call. Do not create any JSON for this.
  * If the task is to generate a plot, display the plot.
  * Also plot intermediate results if possible.
* Final result output (file writing):
  * If the task is to generate a text or a string, write the text or string to "/display_output/final_result.txt".
  * If the task is to generate a table, write the table to "/display_output/final_result.csv".
  * If the final result is a plot, display this plot and afterwards, save it in the folder /display_output as .png and as .svg file and print the filename of the .png in the final output of the program.
  * If the task is to generate a number, list, array or dictionary, write the result to "/display_output/final_result.json".
    In that case, do not add additional data structures. Simply json.dump the result to the file. E.g. if the result is x=2, then just do `json.dump(x, fp)`.
  * If the final result was saved as file, make sure to print the filename in the final output of the program.  
* Keep the code short and concise.



Given some task, code to fulfill the task, and detailed feedback, propose new code that incorporates the feedback.
Make sure to keep the code format.

# Task
                
There is a timelapse image dataset in "input_data/FakeTracks.tif". 
Use spot-detection for detecting the bright objects in the individual images. 
Hint: There about 1-6 objects per frame. Hence, there should be a similar number of tracks.
The use the LAP-algorithm to connect the detected dots over time. 
Viualize the tracks. Each track should have a different color. 
Make sure to remove tracks that are very short before visualizing them.
I am expecting a plot of the tracks as final result.

                
# Code

```
# --------------------------------------------------------------
# Spot detection + LAP tracking for a TIFF timelapse
# --------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt
from skimage import io, feature, exposure, img_as_float
from scipy.optimize import linear_sum_assignment
from tqdm import tqdm
import json
import os

# ---- 1. Load the image stack ------------------------------------------------
tif_path = "input_data/FakeTracks.tif"
stack = io.imread(tif_path)          # shape: (n_frames, height, width)
n_frames = stack.shape[0]

# ---- 2. Detect bright spots in each frame -----------------------------------
def detect_spots(image, min_sigma=2, max_sigma=5, num_sigma=10, thresh=0.03):
    """Return (y, x) coordinates of blobs (Laplacian‑of‑Gaussian)."""
    image = img_as_float(image)
    # improve contrast for robust detection
    image = exposure.equalize_adapthist(image)
    blobs = feature.blob_log(image,
                            min_sigma=min_sigma,
                            max_sigma=max_sigma,
                            num_sigma=num_sigma,
                            threshold=thresh)
    # blob_log returns (y, x, sigma); we keep only y, x
    return blobs[:, :2]

# store detections per frame
detections = [detect_spots(frame) for frame in tqdm(stack, desc="Detecting spots")]

# ---- 3. Simple LAP tracking --------------------------------------------------
max_link_distance = 15.0   # maximum pixel distance a spot may move between frames

tracks = []                # each track = dict with keys: 'frames', 'positions'
active = []                # list of indices of currently alive tracks

for f, spots in enumerate(detections):
    spots = np.array(spots)                 # (n_spots, 2)

    if f == 0:                              # initialise a track for every detection
        for s in spots:
            tracks.append(dict(frames=[0], positions=[s]))
            active.append(len(tracks) - 1)
        continue

    # previous positions of active tracks
    prev_pos = np.array([tracks[i]['positions'][-1] for i in active])
    if len(prev_pos) == 0 or len(spots) == 0:   # no possible links
        # all spots start new tracks
        for s in spots:
            tracks.append(dict(frames=[f], positions=[s]))
            active.append(len(tracks) - 1)
        continue

    # cost matrix = Euclidean distances
    cost = np.linalg.norm(prev_pos[:, None, :] - spots[None, :, :], axis=2)

    # apply max distance gating
    cost[cost > max_link_distance] = np.inf

    # ------------------------------------------------------------------
    # Robust assignment: ignore rows/columns that are completely infeasible
    # ------------------------------------------------------------------
    row_has_finite = np.any(np.isfinite(cost), axis=1)
    col_has_finite = np.any(np.isfinite(cost), axis=0)

    if not row_has_finite.any() or not col_has_finite.any():
        # No feasible assignments at all
        row_ind = np.empty(0, dtype=int)
        col_ind = np.empty(0, dtype=int)
    else:
        # Solve the reduced assignment problem
        sub_cost = cost[row_has_finite][:, col_has_finite]
        sub_row_ind, sub_col_ind = linear_sum_assignment(sub_cost)

        # Map back to the original indices
        row_ind = np.where(row_has_finite)[0][sub_row_ind]
        col_ind = np.where(col_has_finite)[0][sub_col_ind]

    # matches that respect the distance gate
    matched_prev = set()
    matched_spot = set()
    for r, c in zip(row_ind, col_ind):
        if cost[r, c] == np.inf:
            continue
        track_idx = active[r]
        tracks[track_idx]['frames'].append(f)
        tracks[track_idx]['positions'].append(spots[c])
        matched_prev.add(r)
        matched_spot.add(c)

    # tracks that were not matched become inactive
    new_active = [active[i] for i in range(len(active)) if i not in matched_prev]

    # unmatched detections start new tracks
    for s_idx, s in enumerate(spots):
        if s_idx not in matched_spot:
            tracks.append(dict(frames=[f], positions=[s]))
            new_active.append(len(tracks) - 1)

    active = new_active

# ---- 4. Remove very short tracks ---------------------------------------------
min_track_length = 5          # frames
filtered_tracks = [t for t in tracks if len(t['frames']) >= min_track_length]

# ---- 5. Visualise -------------------------------------------------------------
plt.figure(figsize=(8, 6))
cmap = plt.get_cmap("tab20")
for i, tr in enumerate(filtered_tracks):
    pos = np.array(tr['positions'])
    plt.plot(pos[:, 1], pos[:, 0], '-o',
             color=cmap(i % cmap.N), label=f"track {i+1}")

plt.gca().invert_yaxis()      # image coordinates: origin top‑left
plt.title("Detected object tracks")
plt.xlabel("X pixel")
plt.ylabel("Y pixel")
plt.axis('equal')
plt.legend(loc='best', fontsize='small', ncol=2)

# save the figure
out_png = "/display_output/tracks.png"
out_svg = "/display_output/tracks.svg"
plt.savefig(out_png, dpi=150, bbox_inches='tight')
plt.savefig(out_svg, format='svg', bbox_inches='tight')
plt.show()

# ---- 6. Final output ---------------------------------------------------------
print("Track plot showing detected object trajectories.")
print(out_png)
```

# Feedback

### Feedback

**1. Data Analysis (Visualization)**
The code performs spot detection and tracking without visualizing the intermediate detection results. It is critical to visualize the detected spots on a few frames to ensure the `threshold` and `sigma` parameters are correct before tracking.

**2. Image Analysis (Preprocessing)**
The use of `exposure.equalize_adapthist` (CLAHE) inside a loop for every frame can be computationally expensive and may introduce artifacts or inconsistent intensities between frames, which can destabilize spot detection. Global normalization or a simple threshold is often preferred for timelapse data of bright objects.

**3. Code Complexity (Tracking Logic)**
The tracking logic for managing `active` tracks and the mapping of indices is overly complex. Using a simple list of arrays or a dataframe would be more efficient.

**4. Sanity Checks**
There is no check to see if the `detections` list is empty or if the number of spots per frame aligns with the expected "1-6 objects per frame" mentioned in the task.

**5. Final Result**
The final output should print only the filenames of the saved plots.

---

### Proposed Improvements

**Visualization and Preprocessing:**
```python
# Old code
def detect_spots(image, min_sigma=2, max_sigma=5, num_sigma=10, thresh=0.03):
    image = img_as_float(image)
    image = exposure.equalize_adapthist(image)
    blobs = feature.blob_log(image, ...)
    return blobs[:, :2]

# New code
# Perform a sanity check visualization before full processing
sample_frame = img_as_float(stack[0])
spots_sample = feature.blob_log(sample_frame, min_sigma=2, max_sigma=5, threshold=0.03)
plt.imshow(sample_frame, cmap='gray')
plt.plot(spots_sample[:, 1], spots_sample[:, 0], 'ro')
plt.title("Detection Sanity Check")
plt.show()

def detect_spots(image, min_sigma=2, max_sigma=5, num_sigma=10, thresh=0.03):
    image = img_as_float(image)
    # Use simple normalization if contrast is consistent
    image = (image - image.min()) / (image.max() - image.min())
    blobs = feature.blob_log(image, min_sigma=min_sigma, max_sigma=max_sigma, 
                            num_sigma=num_sigma, threshold=thresh)
    return blobs[:, :2]
```

**Simplified Tracking Logic:**
```python
# Old code
# (Long block involving row_has_finite, sub_cost, and mapping back indices)

# New code
# Simplify assignment: linear_sum_assignment handles the cost matrix directly.
# Only filter the resulting pairs by the max_link_distance.
row_ind, col_ind = linear_sum_assignment(cost)
matched_prev = set()
matched_spot = set()

for r, c in zip(row_ind, col_ind):
    if cost[r, c] <= max_link_distance:
        track_idx = active[r]
        tracks[track_idx]['frames'].append(f)
        tracks[track_idx]['positions'].append(spots[c])
        matched_prev.add(r)
        matched_spot.add(c)
```

**Final Output:**
```python
# Old code
print("Track plot showing detected object trajectories.")
print(out_png)

# New code
print(out_png)
print(out_svg)
```

# Your task
Provide the updated code to incorporate the feedback. Also make sure the original task will be fulfilled. Skip all explanations.

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: 5.24s
  • Execution Time: 5.33s
  • Total Time: 180.09s
  • Files:
    • /display_output/notebook_executed.ipynb
    • /display_output/tracks.png
    • /display_output/tracks.svg
LLM backend
TaskFunctionModel
Generate  codeprompt_scadsai_llmopenai/gpt-oss-120b
Fix codeprompt_scadsai_llmopenai/gpt-oss-120b
Determine dependenciesprompt_scadsai_llmopenai/gpt-oss-120b
Generate code  feedbackprompt_scadsai_llmgoogle/gemma-4-31B-it
Summarize codeprompt_scadsai_llmopenai/gpt-oss-120b
Notebook conversionprompt_scadsai_llmopenai/gpt-oss-120b













    


    

    
Execution Output
Detecting spots:   0%|          | 0/50 [00:00<?, ?it/s]
Detecting spots:  32%|███▏      | 16/50 [00:00<00:00, 158.51it/s]
Detecting spots:  74%|███████▍  | 37/50 [00:00<00:00, 188.16it/s]
Detecting spots: 100%|██████████| 50/50 [00:00<00:00, 189.05it/s]
Track plot saved as PNG file.
/display_output/tracks.png
# --------------------------------------------------------------
# Load stack, detect spots, sanity‑check detections, track with LAP,
# filter short tracks and visualise them.
# --------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt
from skimage import io, feature
from scipy.optimize import linear_sum_assignment
from tqdm import tqdm

# ------------------------------------------------------------------
# 1. Load the multi‑page TIFF
# ------------------------------------------------------------------
tif_path = "input_data/FakeTracks.tif"
stack = io.imread(tif_path)                     # (n_frames, H, W)

# ------------------------------------------------------------------
# 2. Spot detection (LoG) – tighter threshold
# ------------------------------------------------------------------
threshold = 0.2          # higher to suppress noise
min_sigma = 1
max_sigma = 5
num_sigma = 5

detections_per_frame = []   # list of (N_i, 2) arrays with [x, y]

for frame in tqdm(stack, desc="Detecting spots"):
    blobs = feature.blob_log(frame,
                             min_sigma=min_sigma,
                             max_sigma=max_sigma,
                             num_sigma=num_sigma,
                             threshold=threshold)
    # blobs: (y, x, sigma) → keep (x, y)
    coords = blobs[:, :2][:, ::-1] if blobs.size else np.empty((0, 2))
    detections_per_frame.append(coords)

# ------------------------------------------------------------------
# 3. Sanity check: visualise detections on a few frames
# ------------------------------------------------------------------
for idx in [0, len(stack)//2, -1]:
    plt.figure(figsize=(5, 5))
    plt.imshow(stack[idx], cmap="gray")
    dets = detections_per_frame[idx]
    if dets.size:
        plt.scatter(dets[:, 0], dets[:, 1], s=30, c='r', marker='o')
    plt.title(f"Detections – frame {idx}")
    plt.axis('off')
    plt.show()

# ------------------------------------------------------------------
# 4. Link detections with LAP (max_distance reduced)
# ------------------------------------------------------------------
max_distance = 5  # tighter spatial constraint
tracks = []       # each track: list of [frame, x, y]

# initialise with first frame detections
for det in detections_per_frame[0]:
    tracks.append([[0, det[0], det[1]]])

active_idx = list(range(len(tracks)))   # indices of currently active tracks

for f in range(1, len(detections_per_frame)):
    cur = detections_per_frame[f]
    if len(active_idx) == 0 or cur.shape[0] == 0:
        # start new tracks for all detections
        for det in cur:
            tracks.append([[f, det[0], det[1]]])
            active_idx.append(len(tracks) - 1)
        continue

    prev_pos = np.array([tracks[t][-1][1:3] for t in active_idx])   # (N_active, 2)
    cost = np.linalg.norm(prev_pos[:, None, :] - cur[None, :, :], axis=2)

    row_ind, col_ind = linear_sum_assignment(cost)

    assigned_tracks, assigned_dets = set(), set()
    for r, c in zip(row_ind, col_ind):
        if cost[r, c] <= max_distance:
            t_idx = active_idx[r]
            tracks[t_idx].append([f, cur[c, 0], cur[c, 1]])
            assigned_tracks.add(r)
            assigned_dets.add(c)

    # keep only tracks that received an assignment
    new_active = [active_idx[i] for i in range(len(active_idx)) if i in assigned_tracks]
    active_idx = new_active

    # create new tracks for unassigned detections
    for d in range(cur.shape[0]):
        if d not in assigned_dets:
            tracks.append([[f, cur[d, 0], cur[d, 1]]])
            active_idx.append(len(tracks) - 1)

# ------------------------------------------------------------------
# 5. Remove very short tracks (less than 5 frames)
# ------------------------------------------------------------------
min_len = 5
filtered_tracks = [np.array(tr) for tr in tracks if len(tr) >= min_len]

# ------------------------------------------------------------------
# 6. Visualise tracks – each with a distinct colour
# ------------------------------------------------------------------
plt.figure(figsize=(8, 6))
cmap = plt.get_cmap("tab20")
for i, tr in enumerate(filtered_tracks):
    xs, ys = tr[:, 1], tr[:, 2]
    col = cmap(i % cmap.N)
    plt.plot(xs, ys, '-', color=col, linewidth=2)
    plt.scatter(xs, ys, color=col, s=30)

plt.title("Tracked bright objects")
plt.xlabel("X pixel")
plt.ylabel("Y pixel")
plt.gca().invert_yaxis()
plt.tight_layout()

# ------------------------------------------------------------------
# 7. Save and display the figure
# ------------------------------------------------------------------
png_path = "/display_output/tracks.png"
svg_path = "/display_output/tracks.svg"
plt.savefig(png_path, dpi=300)
plt.savefig(svg_path)
plt.show()

# --------------------------------------------------------------
# Output description (second‑last print)
print("Track plot saved as PNG file.")
# Final result (last print – only the filename)
print(png_path)
You are an expert in python programming. You have a list of framework constraints which you MUST follow.
Your task is to generate a fully functional code snippet that will be used to fulfill the prompt.
Assume your code will be executed in a Jupyter notebook cell.

# Framework constraints
* You may use the following libraries, but only if necessary: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
* When saving files, use the following folder: "/display_output/". Do not create this folder. It exists already.
* pip install is STRICTLY PROHIBITED. You can only use the libraries mentioned above.
* Statistics: When applying statisticals test, ENSURE that pre-conditions for the tests are checked before the tests are performed.
* Final result output (print or display calls): 
  * The second-last print or display call should be a description of the result (e.g. the measurment and a physical unit if relevant).
  * The last print or display call should be the final result ONLY.
  * If the task is to generate a count, ratio or measurements, print the final result using a separate `print` call. 
  * If the task is to answer a yes/no question, print "Yes" or "No" using a separate `print` call. Do not create any JSON for this.
  * If the task is to generate a plot, display the plot.
  * Also plot intermediate results if possible.
* Final result output (file writing):
  * If the task is to generate a text or a string, write the text or string to "/display_output/final_result.txt".
  * If the task is to generate a table, write the table to "/display_output/final_result.csv".
  * If the final result is a plot, display this plot and afterwards, save it in the folder /display_output as .png and as .svg file and print the filename of the .png in the final output of the program.
  * If the task is to generate a number, list, array or dictionary, write the result to "/display_output/final_result.json".
    In that case, do not add additional data structures. Simply json.dump the result to the file. E.g. if the result is x=2, then just do `json.dump(x, fp)`.
  * If the final result was saved as file, make sure to print the filename in the final output of the program.  
* Keep the code short and concise.



Given some task, code to fulfill the task, and detailed feedback, propose new code that incorporates the feedback.
Make sure to keep the code format.

# Task
                
There is a timelapse image dataset in "input_data/FakeTracks.tif". 
Use spot-detection for detecting the bright objects in the individual images. 
Hint: There about 1-6 objects per frame. Hence, there should be a similar number of tracks.
The use the LAP-algorithm to connect the detected dots over time. 
Viualize the tracks. Each track should have a different color. 
Make sure to remove tracks that are very short before visualizing them.
I am expecting a plot of the tracks as final result.

                
# Code

```
# --------------------------------------------------------------
# Load the timelapse stack, detect spots, link them and plot tracks
# --------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt
from skimage import io, feature
from scipy.optimize import linear_sum_assignment
from tqdm import tqdm

# ------------------------------------------------------------------
# 1. Load the multi‑page TIFF
# ------------------------------------------------------------------
tif_path = "input_data/FakeTracks.tif"
stack = io.imread(tif_path)                     # shape: (n_frames, height, width)

# ------------------------------------------------------------------
# 2. Detect bright spots in each frame (LoG blob detection)
# ------------------------------------------------------------------
# parameters chosen for typical small bright objects
min_sigma = 1
max_sigma = 5
num_sigma = 5
threshold = 0.02

detections_per_frame = []                       # list of (N_i, 2) arrays with [x, y]

for frame in tqdm(stack, desc="Detecting spots"):
    blobs = feature.blob_log(frame,
                             min_sigma=min_sigma,
                             max_sigma=max_sigma,
                             num_sigma=num_sigma,
                             threshold=threshold)
    # blob_log returns (y, x, sigma); keep x, y only
    if blobs.size:
        coords = blobs[:, :2][:, ::-1]           # (x, y)
    else:
        coords = np.empty((0, 2))
    detections_per_frame.append(coords)

# ------------------------------------------------------------------
# 3. Link detections over time with a simple LAP implementation
# ------------------------------------------------------------------
max_distance = 20                               # max allowed displacement (pixels)

tracks = []                                      # each track: list of (frame, x, y)

# initialise tracks with first frame detections
for det in detections_per_frame[0]:
    tracks.append([[0, det[0], det[1]]])

# helper: keep track of the last known position of each active track
active_idx = list(range(len(tracks)))           # indices of currently active tracks

for f_idx in range(1, len(detections_per_frame)):
    cur_dets = detections_per_frame[f_idx]
    if len(active_idx) == 0 or cur_dets.shape[0] == 0:
        # no active tracks or no detections → start new tracks from all detections
        for det in cur_dets:
            tracks.append([[f_idx, det[0], det[1]]])
            active_idx.append(len(tracks) - 1)
        continue

    # positions of active tracks at previous frame
    prev_pos = np.array([tracks[t][-1][1:3] for t in active_idx])   # (N_active, 2)

    # cost matrix (euclidean distances)
    cost = np.linalg.norm(prev_pos[:, None, :] - cur_dets[None, :, :], axis=2)

    # apply linear assignment
    row_ind, col_ind = linear_sum_assignment(cost)

    assigned_tracks = set()
    assigned_dets   = set()

    # accept only matches within max_distance
    for r, c in zip(row_ind, col_ind):
        if cost[r, c] <= max_distance:
            t_idx = active_idx[r]
            tracks[t_idx].append([f_idx, cur_dets[c, 0], cur_dets[c, 1]])
            assigned_tracks.add(r)
            assigned_dets.add(c)

    # tracks that were not assigned become inactive
    new_active_idx = []
    for idx, t in enumerate(active_idx):
        if idx not in assigned_tracks:
            # keep the track but do not consider it for next frame
            pass
        else:
            new_active_idx.append(t)
    active_idx = new_active_idx

    # start new tracks for detections that were not assigned
    for d_idx in range(cur_dets.shape[0]):
        if d_idx not in assigned_dets:
            tracks.append([[f_idx, cur_dets[d_idx, 0], cur_dets[d_idx, 1]]])
            active_idx.append(len(tracks) - 1)

# ------------------------------------------------------------------
# 4. Remove very short tracks (less than 5 frames)
# ------------------------------------------------------------------
min_len = 5
filtered_tracks = [np.array(tr) for tr in tracks if len(tr) >= min_len]

# ------------------------------------------------------------------
# 5. Visualise the remaining tracks – each with a distinct colour
# ------------------------------------------------------------------
plt.figure(figsize=(8, 6))
cmap = plt.get_cmap("tab20")
for i, tr in enumerate(filtered_tracks):
    xs, ys = tr[:, 1], tr[:, 2]
    color = cmap(i % cmap.N)
    plt.plot(xs, ys, '-', color=color, linewidth=2)
    plt.scatter(xs, ys, color=color, s=30)

plt.title("Tracked bright objects")
plt.xlabel("X pixel")
plt.ylabel("Y pixel")
plt.gca().invert_yaxis()           # image coordinates: origin at top‑left
plt.tight_layout()

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

# --------------------------------------------------------------
# Output description (second‑last print)
print("Track plot saved as PNG file.")
# Final result (last print – only the filename)
print(png_path)
```

# Feedback

The code is logically sound, but the final result (the plot) shows a massive over-segmentation/detection problem. Instead of 1-6 tracks, you have hundreds of erratic lines, indicating the spot detection threshold is too low and the matching distance is too permissive for the noise level in the images.

### Image Analysis & Data Analysis
The plot is filled with noise. You are detecting many false positives, which the LAP algorithm then tries to connect, resulting in a "spaghetti" plot. You must visualize the detections (the "dots") on a few sample frames before proceeding to tracking to ensure only the actual objects are picked up.

### Statistics & Logic
The `max_distance` of 20 pixels is likely too high if the objects are expected to move smoothly, especially when combined with a low detection threshold.

### Code Quality & Complexity
The manual loop for tracking is a bit verbose. While functional, it can be simplified.

---

### Improvements

**1. Detection and Sanity Check**
Add a visualization step for detections. Increase the `threshold` significantly to eliminate background noise.

```python
# OLD
threshold = 0.02
# ... loop ...
# (No visualization of dots)

# NEW
threshold = 0.2 # Increase based on image intensity
# ... loop ...
# Sanity check: Plot detections on frame 0
plt.imshow(stack[0], cmap='gray')
plt.scatter(detections_per_frame[0][:, 0], detections_per_frame[0][:, 1], s=10, c='r')
plt.title("Detection Sanity Check")
plt.show()
```

**2. Tracking Constraints**
Tighten the `max_distance` to prevent the algorithm from jumping between noise spikes.

```python
# OLD
max_distance = 20

# NEW
max_distance = 5 # Adjust based on expected object velocity
```

**3. Final Output**
The task asks for the filename of the result. The current code prints a description and then the path; it should be more direct.

```python
# OLD
print("Track plot saved as PNG file.")
print(png_path)

# NEW
print(png_path)
```

### Summary of modifications:
1. **Increased `threshold`**: To stop detecting noise as objects.
2. **Added sanity check**: Visualizing detected spots on the image before tracking.
3. **Reduced `max_distance`**: To ensure tracks are spatially continuous and not jumping across the frame.
4. **Cleaned output**: Removed unnecessary print statements.

# Your task
Provide the updated code to incorporate the feedback. Also make sure the original task will be fulfilled. Skip all explanations.

Execution Details
  • Execution reason: Exe
  • Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
  • Final result: [[[255 255 255 255]
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 ...

 [[255 255 255 255]
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  ...
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  [255 255 255 255]]]
  • Build Time: 0.00s
  • Run Time: 5.33s
  • Execution Time: 5.42s
  • Total Time: 107.89s
  • Files:
    • /display_output/notebook_executed.ipynb
    • /display_output/tracks.png
    • /display_output/tracks.svg
LLM backend
TaskFunctionModel
Generate  codeprompt_scadsai_llmopenai/gpt-oss-120b
Fix codeprompt_scadsai_llmopenai/gpt-oss-120b
Determine dependenciesprompt_scadsai_llmopenai/gpt-oss-120b
Generate code  feedbackprompt_scadsai_llmgoogle/gemma-4-31B-it
Summarize codeprompt_scadsai_llmopenai/gpt-oss-120b
Notebook conversionprompt_scadsai_llmopenai/gpt-oss-120b













    


    

    
Execution Output
Plot of tracked objects saved as PNG.
/display_output/tracks_plot.png
import numpy as np
import matplotlib.pyplot as plt
from skimage import io, feature, img_as_float
from scipy.optimize import linear_sum_assignment

# -------------------------------------------------
# 1. Load timelapse
tif_path = "input_data/FakeTracks.tif"
frames = img_as_float(io.imread(tif_path))          # (T, H, W)
n_frames = frames.shape[0]

# -------------------------------------------------
# 2. Spot detection (blob_log) with sanity check
threshold = 0.1            # increased from 0.02 – adjust if warnings appear
min_sigma, max_sigma, num_sigma = 2, 5, 3

detections = []            # list of (y, x) per frame
for i in range(n_frames):
    blobs = feature.blob_log(frames[i],
                             min_sigma=min_sigma,
                             max_sigma=max_sigma,
                             num_sigma=num_sigma,
                             threshold=threshold)
    pts = blobs[:, :2] if blobs.size else np.empty((0, 2))
    detections.append(pts)
    if len(pts) > 20:
        print(f"Warning: Frame {i} has {len(pts)} detections – consider raising the threshold.")

# -------------------------------------------------
# 3. LAP tracking
max_dist = 15.0            # pixels – a bit larger to tolerate small moves
next_track_id = 0
tracks = {}                # track_id -> list of (frame, y, x)
active = {}                # track_id -> last position (y, x)

for t, pts in enumerate(detections):
    pts = np.asarray(pts)
    if t == 0:
        for p in pts:
            tracks[next_track_id] = [(t, p[0], p[1])]
            active[next_track_id] = p
            next_track_id += 1
        continue

    if len(active) == 0 or len(pts) == 0:
        for p in pts:
            tracks[next_track_id] = [(t, p[0], p[1])]
            active[next_track_id] = p
            next_track_id += 1
        continue

    track_ids = list(active.keys())
    track_pos = np.stack([active[i] for i in track_ids])          # (n_tracks, 2)

    cost = np.linalg.norm(track_pos[:, None, :] - pts[None, :, :], axis=2)

    row_ind, col_ind = linear_sum_assignment(cost)
    assigned_tracks, assigned_dets = set(), set()

    for r, c in zip(row_ind, col_ind):
        if cost[r, c] <= max_dist:
            tr_id = track_ids[r]
            p = pts[c]
            tracks[tr_id].append((t, p[0], p[1]))
            active[tr_id] = p
            assigned_tracks.add(tr_id)
            assigned_dets.add(c)

    # deactivate tracks that were not assigned
    for tr_id in track_ids:
        if tr_id not in assigned_tracks:
            active.pop(tr_id, None)

    # start new tracks for unassigned detections
    for idx, p in enumerate(pts):
        if idx not in assigned_dets:
            tracks[next_track_id] = [(t, p[0], p[1])]
            active[next_track_id] = p
            next_track_id += 1

# -------------------------------------------------
# 4. Remove very short tracks
min_length = 3                         # keep tracks spanning ≥3 frames
filtered_tracks = {tid: tr for tid, tr in tracks.items() if len(tr) >= min_length}

# -------------------------------------------------
# 5. Visualise tracks (different colour per track)
plt.figure(figsize=(6, 6))
cmap = plt.get_cmap("tab20")
for i, (tid, tr) in enumerate(filtered_tracks.items()):
    tr_arr = np.array(tr)
    plt.plot(tr_arr[:, 2], tr_arr[:, 1],
             color=cmap(i % 20), lw=2)
    plt.scatter(tr_arr[:, 2], tr_arr[:, 1],
                color=cmap(i % 20), s=30)

plt.title("Tracked bright objects")
plt.xlabel("X (pixel)")
plt.ylabel("Y (pixel)")
plt.gca().invert_yaxis()      # image origin top‑left
plt.axis('equal')
plt.tight_layout()

# -------------------------------------------------
# 6. Save plot (png & svg) BEFORE showing
out_png = "/display_output/tracks_plot.png"
out_svg = "/display_output/tracks_plot.svg"
plt.savefig(out_png, dpi=150)
plt.savefig(out_svg)

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

# -------------------------------------------------
# 8. Final output
print("Plot of tracked objects saved as PNG.")
print(out_png)
You are an expert in python programming. You have a list of framework constraints which you MUST follow.
Your task is to generate a fully functional code snippet that will be used to fulfill the prompt.
Assume your code will be executed in a Jupyter notebook cell.

# Framework constraints
* You may use the following libraries, but only if necessary: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
* When saving files, use the following folder: "/display_output/". Do not create this folder. It exists already.
* pip install is STRICTLY PROHIBITED. You can only use the libraries mentioned above.
* Statistics: When applying statisticals test, ENSURE that pre-conditions for the tests are checked before the tests are performed.
* Final result output (print or display calls): 
  * The second-last print or display call should be a description of the result (e.g. the measurment and a physical unit if relevant).
  * The last print or display call should be the final result ONLY.
  * If the task is to generate a count, ratio or measurements, print the final result using a separate `print` call. 
  * If the task is to answer a yes/no question, print "Yes" or "No" using a separate `print` call. Do not create any JSON for this.
  * If the task is to generate a plot, display the plot.
  * Also plot intermediate results if possible.
* Final result output (file writing):
  * If the task is to generate a text or a string, write the text or string to "/display_output/final_result.txt".
  * If the task is to generate a table, write the table to "/display_output/final_result.csv".
  * If the final result is a plot, display this plot and afterwards, save it in the folder /display_output as .png and as .svg file and print the filename of the .png in the final output of the program.
  * If the task is to generate a number, list, array or dictionary, write the result to "/display_output/final_result.json".
    In that case, do not add additional data structures. Simply json.dump the result to the file. E.g. if the result is x=2, then just do `json.dump(x, fp)`.
  * If the final result was saved as file, make sure to print the filename in the final output of the program.  
* Keep the code short and concise.



Given some task, code to fulfill the task, and detailed feedback, propose new code that incorporates the feedback.
Make sure to keep the code format.

# Task
                
There is a timelapse image dataset in "input_data/FakeTracks.tif". 
Use spot-detection for detecting the bright objects in the individual images. 
Hint: There about 1-6 objects per frame. Hence, there should be a similar number of tracks.
The use the LAP-algorithm to connect the detected dots over time. 
Viualize the tracks. Each track should have a different color. 
Make sure to remove tracks that are very short before visualizing them.
I am expecting a plot of the tracks as final result.

                
# Code

```
import numpy as np
import matplotlib.pyplot as plt
from skimage import io, feature, img_as_float
from scipy.optimize import linear_sum_assignment
import json
import os

# ---- 1. Load timelapse -------------------------------------------------
tif_path = "input_data/FakeTracks.tif"
frames = io.imread(tif_path)                 # shape: (T, H, W)
frames = img_as_float(frames)                # ensure float for blob detection
n_frames = frames.shape[0]

# ---- 2. Spot detection (blob_log) ---------------------------------------
detections = []                               # list of (y,x) per frame
min_sigma, max_sigma, num_sigma = 2, 5, 3
threshold = 0.02

for i in range(n_frames):
    blobs = feature.blob_log(frames[i],
                             min_sigma=min_sigma,
                             max_sigma=max_sigma,
                             num_sigma=num_sigma,
                             threshold=threshold)
    # blob_log returns (y, x, sigma); we keep only (y, x)
    det = blobs[:, :2] if blobs.size else np.empty((0, 2))
    detections.append(det)

# ---- 3. Simple LAP tracking ----------------------------------------------
max_dist = 12.0                               # max linking distance (pixels)
next_track_id = 0
tracks = {}                                   # track_id -> list of (frame, y, x)
active = {}                                   # track_id -> last position (y,x)

for t, pts in enumerate(detections):
    pts = np.asarray(pts)
    if t == 0:
        # initialise a track for every detection in the first frame
        for p in pts:
            tracks[next_track_id] = [(t, p[0], p[1])]
            active[next_track_id] = p
            next_track_id += 1
        continue

    # build cost matrix between active tracks and current detections
    if len(active) == 0 or len(pts) == 0:
        # no active tracks or no detections – start new tracks for all detections
        for p in pts:
            tracks[next_track_id] = [(t, p[0], p[1])]
            active[next_track_id] = p
            next_track_id += 1
        continue

    track_ids = list(active.keys())
    track_pos = np.stack([active[i] for i in track_ids])          # (n_tracks, 2)
    cost = np.linalg.norm(track_pos[:, None, :] - pts[None, :, :], axis=2)  # (n_tracks, n_dets)

    # Hungarian assignment
    row_ind, col_ind = linear_sum_assignment(cost)
    assigned_tracks = set()
    assigned_dets = set()

    # process assignments respecting max_dist
    for r, c in zip(row_ind, col_ind):
        if cost[r, c] <= max_dist:
            tr_id = track_ids[r]
            p = pts[c]
            tracks[tr_id].append((t, p[0], p[1]))
            active[tr_id] = p
            assigned_tracks.add(tr_id)
            assigned_dets.add(c)

    # tracks that were not assigned become inactive (removed from active set)
    for tr_id in track_ids:
        if tr_id not in assigned_tracks:
            active.pop(tr_id)

    # detections that were not assigned start new tracks
    for idx, p in enumerate(pts):
        if idx not in assigned_dets:
            tracks[next_track_id] = [(t, p[0], p[1])]
            active[next_track_id] = p
            next_track_id += 1

# ---- 4. Remove very short tracks -----------------------------------------
min_length = 3                                 # keep tracks covering ≥3 frames
filtered_tracks = {tid: tr for tid, tr in tracks.items() if len(tr) >= min_length}

# ---- 5. Visualise ---------------------------------------------------------
plt.figure(figsize=(6, 6))
cmap = plt.get_cmap("tab20")
for i, (tid, tr) in enumerate(filtered_tracks.items()):
    tr = np.array(tr)
    plt.plot(tr[:, 2], tr[:, 1],
             color=cmap(i % 20), lw=2,
             label=f"Track {tid}")
    plt.scatter(tr[:, 2], tr[:, 1],
                color=cmap(i % 20), s=30)

plt.title("Tracked spots over time")
plt.xlabel("X pixel")
plt.ylabel("Y pixel")
plt.gca().invert_yaxis()          # origin is top‑left in images
plt.axis('equal')
plt.tight_layout()
plt.show()

# ---- 6. Save plot ---------------------------------------------------------
out_png = "/display_output/tracks_plot.png"
out_svg = "/display_output/tracks_plot.svg"
plt.savefig(out_png, dpi=150)
plt.savefig(out_svg)

# ---- 7. Final output ------------------------------------------------------
print("Plot of tracks where each track is shown in a unique colour.")
print(out_png)
```

# Feedback

The provided code has a critical logical flaw and a visualization bug. The resulting plot shows a "spaghetti" effect, indicating that the LAP tracking is failing or that the spot detection is picking up too much noise.

### Feedback

**1. Final Result Logic (Crucial)**
The plot shows an enormous number of overlapping tracks. The task states there are only 1-6 objects per frame. The current code is likely detecting hundreds of noise-blobs per frame, leading to fragmented and chaotic tracks. You must visualize the detections or the image before tracking to tune the `threshold`.

**2. Visualization Sequence**
You called `plt.show()` before `plt.savefig()`. In many Matplotlib backends, `plt.show()` clears the current figure, resulting in the saved files being blank or containing an empty axis.

**3. Tracking Logic**
While the LAP implementation is mathematically correct, the `max_dist` is a hard threshold. If the threshold for blob detection is too low, the `linear_sum_assignment` will force matches between noise points, creating the "zig-zag" patterns seen in the result.

**4. Code Quality**
The code lacks sanity checks. Printing the number of detections per frame would have immediately revealed why the final plot looks incorrect.

---

### Suggested Improvements

**Image Analysis & Sanity Check:**
Add a check for the number of detected blobs. If the count is significantly higher than 6, the `threshold` needs to be increased.

```python
# Old code
for i in range(n_frames):
    blobs = feature.blob_log(...)
    det = blobs[:, :2] if blobs.size else np.empty((0, 2))
    detections.append(det)

# New code
for i in range(n_frames):
    blobs = feature.blob_log(...)
    det = blobs[:, :2] if blobs.size else np.empty((0, 2))
    detections.append(det)
    # Sanity check: alert if too many objects are found
    if len(det) > 20: 
        print(f"Warning: Frame {i} has {len(det)} detections. Consider increasing threshold.")
```

**Correcting Save/Show Order:**
Move `plt.show()` to the very end of the plotting block.

```python
# Old code
plt.show()
out_png = "/display_output/tracks_plot.png"
plt.savefig(out_png, dpi=150)

# New code
out_png = "/display_output/tracks_plot.png"
out_svg = "/display_output/tracks_plot.svg"
plt.savefig(out_png, dpi=150)
plt.savefig(out_svg)
plt.show()
```

**Parameter Tuning:**
Based on the "spaghetti" result, the `threshold` is far too low. Increase it significantly to ensure only bright spots are detected.

```python
# Old code
threshold = 0.02

# New code (Example: increase based on image intensity)
threshold = 0.1  # Adjust this value based on the sanity check output
```

**Final Result Display:**
Ensure the final print statement is the very last action.

```python
# Final result
print(out_png)
```

# Your task
Provide the updated code to incorporate the feedback. Also make sure the original task will be fulfilled. Skip all explanations.

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]]

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  [255 255 255 255]
  [255 255 255 255]
  ...
  [255 255 255 255]
  [255 255 255 255]
  [255 255 255 255]]

 [[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: 5.06s
  • Execution Time: 5.14s
  • Total Time: 106.58s
  • Files:
    • /display_output/notebook_executed.ipynb
    • /display_output/tracks_plot.png
    • /display_output/tracks_plot.svg
LLM backend
TaskFunctionModel
Generate  codeprompt_scadsai_llmopenai/gpt-oss-120b
Fix codeprompt_scadsai_llmopenai/gpt-oss-120b
Determine dependenciesprompt_scadsai_llmopenai/gpt-oss-120b
Generate code  feedbackprompt_scadsai_llmgoogle/gemma-4-31B-it
Summarize codeprompt_scadsai_llmopenai/gpt-oss-120b
Notebook conversionprompt_scadsai_llmopenai/gpt-oss-120b













    


    

    
Execution Output
Detecting spots:   0%|          | 0/50 [00:00<?, ?it/s]
Detecting spots:  12%|█▏        | 6/50 [00:00<00:00, 55.54it/s]
Detecting spots:  24%|██▍       | 12/50 [00:00<00:00, 54.91it/s]
Detecting spots:  36%|███▌      | 18/50 [00:00<00:00, 56.28it/s]
Detecting spots:  48%|████▊     | 24/50 [00:00<00:00, 55.42it/s]
Detecting spots:  60%|██████    | 30/50 [00:00<00:00, 54.00it/s]
Detecting spots:  72%|███████▏  | 36/50 [00:00<00:00, 53.12it/s]
Detecting spots:  84%|████████▍ | 42/50 [00:00<00:00, 52.33it/s]
Detecting spots:  96%|█████████▌| 48/50 [00:00<00:00, 53.33it/s]
Detecting spots: 100%|██████████| 50/50 [00:00<00:00, 53.75it/s]
Plot of spot tracks saved as PNG (and SVG).
/display_output/tracks.png
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from skimage import io, feature, exposure
from scipy.optimize import linear_sum_assignment
from scipy.spatial.distance import cdist
from tqdm import tqdm
import random

# ------------------------------------------------------------------
# 1. Load timelapse stack
stack_path = "input_data/FakeTracks.tif"
frames = io.imread(stack_path)               # (num_frames, h, w)

# ------------------------------------------------------------------
# 2. Spot detection (LoG)
def detect_spots(img, min_sigma=2, max_sigma=5, num_sigma=10, thresh=0.02):
    img_eq = exposure.equalize_adapthist(img)
    blobs = feature.blob_log(img_eq,
                             min_sigma=min_sigma,
                             max_sigma=max_sigma,
                             num_sigma=num_sigma,
                             threshold=thresh)
    return blobs[:, :2]   # (y, x)

# ------------------------------------------------------------------
# 3. Visual sanity check of detection on a random frame
sample_f = random.randint(0, frames.shape[0] - 1)
sample_img = frames[sample_f]
sample_pts = detect_spots(sample_img)

plt.figure(figsize=(6, 5))
plt.imshow(sample_img, cmap='gray')
plt.scatter(sample_pts[:, 1], sample_pts[:, 0],
            edgecolor='red', facecolor='none', s=30, linewidth=1.5)
plt.title(f"Spot detection check – frame {sample_f}")
plt.axis('off')
plt.show()

# ------------------------------------------------------------------
# 4. Detect spots for all frames
detections = []   # list of DataFrames per frame
for f, img in enumerate(tqdm(frames, desc="Detecting spots")):
    pts = detect_spots(img)
    df = pd.DataFrame(pts, columns=["y", "x"])
    df["frame"] = f
    detections.append(df)

detections_df = pd.concat(detections, ignore_index=True)

# ------------------------------------------------------------------
# 5. LAP tracking using Hungarian algorithm
max_dist = 15          # max linking distance (pixels)
min_track_len = 3      # discard tracks shorter than this

tracks = []            # finished tracks
active_tracks = []    # tracks updated in previous frame

for f in range(frames.shape[0]):
    cur_pts = detections_df[detections_df["frame"] == f][["y", "x"]].values

    if f == 0:
        # initialise active tracks with detections from first frame
        for pt in cur_pts:
            active_tracks.append([[f, pt[0], pt[1]]])
        continue

    if len(active_tracks) == 0:
        # no previous tracks – start new ones
        for pt in cur_pts:
            active_tracks.append([[f, pt[0], pt[1]]])
        continue

    if len(cur_pts) == 0:
        # no detections – finish all active tracks
        tracks.extend(active_tracks)
        active_tracks = []
        continue

    prev_pts = np.array([trk[-1][1:] for trk in active_tracks])   # (n_active, 2)
    D = cdist(prev_pts, cur_pts)                               # (n_active, n_cur)

    row_ind, col_ind = linear_sum_assignment(D)

    assigned_tracks = set()
    assigned_pts = set()

    for r, c in zip(row_ind, col_ind):
        if D[r, c] <= max_dist:
            active_tracks[r].append([f, cur_pts[c][0], cur_pts[c][1]])
            assigned_tracks.add(r)
            assigned_pts.add(c)

    # tracks that were not assigned become finished
    new_active = []
    for idx, trk in enumerate(active_tracks):
        if idx in assigned_tracks:
            new_active.append(trk)
        else:
            tracks.append(trk)
    active_tracks = new_active

    # start new tracks for unassigned detections
    for j in range(len(cur_pts)):
        if j not in assigned_pts:
            active_tracks.append([[f, cur_pts[j][0], cur_pts[j][1]]])

# add remaining active tracks to finished list
tracks.extend(active_tracks)

# ------------------------------------------------------------------
# 6. Remove very short tracks
filtered_tracks = [trk for trk in tracks if len(trk) >= min_track_len]

# ------------------------------------------------------------------
# 7. Visualise tracks (different colour per track)
cmap = plt.get_cmap('tab20', max(len(filtered_tracks), 1))

fig, ax = plt.subplots(figsize=(8, 6))
for idx, trk in enumerate(filtered_tracks):
    trk_arr = np.array(trk)          # (n, 3): frame, y, x
    ax.plot(trk_arr[:, 2], trk_arr[:, 1],
            '-', color=cmap(idx), linewidth=2)
    ax.scatter(trk_arr[:, 2], trk_arr[:, 1],
               color=cmap(idx), s=30)

ax.set_xlabel('X pixel')
ax.set_ylabel('Y pixel')
ax.set_title('Spot tracks over time')
ax.invert_yorder() if hasattr(ax, "invert_yorder") else ax.invert_yaxis()
ax.set_aspect('equal')
ax.axis('off')

# ------------------------------------------------------------------
# 8. Save plot
png_path = "/display_output/tracks.png"
svg_path = "/display_output/tracks.svg"
fig.savefig(png_path, dpi=150, bbox_inches='tight')
fig.savefig(svg_path, format='svg', bbox_inches='tight')
plt.show()

# ------------------------------------------------------------------
# 9. Final output
print("Plot of spot tracks saved as PNG (and SVG).")
print(png_path)
You are an expert in python programming. You have a list of framework constraints which you MUST follow.
Your task is to generate a fully functional code snippet that will be used to fulfill the prompt.
Assume your code will be executed in a Jupyter notebook cell.

# Framework constraints
* You may use the following libraries, but only if necessary: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
* When saving files, use the following folder: "/display_output/". Do not create this folder. It exists already.
* pip install is STRICTLY PROHIBITED. You can only use the libraries mentioned above.
* Statistics: When applying statisticals test, ENSURE that pre-conditions for the tests are checked before the tests are performed.
* Final result output (print or display calls): 
  * The second-last print or display call should be a description of the result (e.g. the measurment and a physical unit if relevant).
  * The last print or display call should be the final result ONLY.
  * If the task is to generate a count, ratio or measurements, print the final result using a separate `print` call. 
  * If the task is to answer a yes/no question, print "Yes" or "No" using a separate `print` call. Do not create any JSON for this.
  * If the task is to generate a plot, display the plot.
  * Also plot intermediate results if possible.
* Final result output (file writing):
  * If the task is to generate a text or a string, write the text or string to "/display_output/final_result.txt".
  * If the task is to generate a table, write the table to "/display_output/final_result.csv".
  * If the final result is a plot, display this plot and afterwards, save it in the folder /display_output as .png and as .svg file and print the filename of the .png in the final output of the program.
  * If the task is to generate a number, list, array or dictionary, write the result to "/display_output/final_result.json".
    In that case, do not add additional data structures. Simply json.dump the result to the file. E.g. if the result is x=2, then just do `json.dump(x, fp)`.
  * If the final result was saved as file, make sure to print the filename in the final output of the program.  
* Keep the code short and concise.



Given some task, code to fulfill the task, and detailed feedback, propose new code that incorporates the feedback.
Make sure to keep the code format.

# Task
                
There is a timelapse image dataset in "input_data/FakeTracks.tif". 
Use spot-detection for detecting the bright objects in the individual images. 
Hint: There about 1-6 objects per frame. Hence, there should be a similar number of tracks.
The use the LAP-algorithm to connect the detected dots over time. 
Viualize the tracks. Each track should have a different color. 
Make sure to remove tracks that are very short before visualizing them.
I am expecting a plot of the tracks as final result.

                
# Code

```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cm
from skimage import io, feature, filters, morphology, exposure
from scipy.spatial.distance import cdist
from tqdm import tqdm

# ------------------------------------------------------------------
# 1. Load the timelapse stack
stack_path = "input_data/FakeTracks.tif"
frames = io.imread(stack_path)               # shape: (num_frames, h, w)

# ------------------------------------------------------------------
# 2. Spot detection per frame (Laplacian of Gaussian)
def detect_spots(img, min_sigma=2, max_sigma=5, num_sigma=10, thresh=0.02):
    # enhance contrast
    img_eq = exposure.equalize_adapthist(img)
    blobs = feature.blob_log(img_eq,
                             min_sigma=min_sigma,
                             max_sigma=max_sigma,
                             num_sigma=num_sigma,
                             threshold=thresh)
    # blob_log returns (y, x, sigma); keep only coordinates
    return blobs[:, :2]   # (y, x)

detections = []   # list of DataFrames, one per frame
for i, img in enumerate(tqdm(frames, desc="Detecting spots")):
    pts = detect_spots(img)
    df = pd.DataFrame(pts, columns=["y", "x"])
    df["frame"] = i
    detections.append(df)

# concatenate for easier handling
detections_df = pd.concat(detections, ignore_index=True)

# ------------------------------------------------------------------
# 3. Simple LAP‑like linking (frame‑to‑frame nearest neighbour with max distance)
max_dist = 15          # pixels, adjust if needed
min_track_len = 3      # frames

tracks = []            # each track = list of (frame, y, x)
active_tracks = []    # tracks that were updated in previous frame

for f in range(frames.shape[0]):
    cur_pts = detections_df[detections_df["frame"] == f][["y", "x"]].values
    # start new tracks for first frame
    if f == 0:
        for pt in cur_pts:
            active_tracks.append([[f, pt[0], pt[1]]])
        continue

    # build distance matrix between last points of active tracks and current points
    prev_pts = np.array([t[-1][1:] for t in active_tracks])   # (n_tracks, 2)
    if len(prev_pts) == 0 or len(cur_pts) == 0:
        # no possible linking -> all active become finished, start fresh tracks
        tracks.extend(active_tracks)
        active_tracks = [[ [f, pt[0], pt[1]] ] for pt in cur_pts]
        continue

    D = cdist(prev_pts, cur_pts)          # (n_tracks, n_cur)
    # assignment using greedy nearest neighbour within max_dist
    assigned_tracks = set()
    assigned_pts = set()
    for i_track in range(D.shape[0]):
        j = np.argmin(D[i_track])
        if D[i_track, j] <= max_dist:
            if j not in assigned_pts:      # one‑to‑one constraint
                active_tracks[i_track].append([f, cur_pts[j][0], cur_pts[j][1]])
                assigned_tracks.add(i_track)
                assigned_pts.add(j)

    # tracks that were *not* assigned become finished
    new_active = []
    for idx, trk in enumerate(active_tracks):
        if idx not in assigned_tracks:
            tracks.append(trk)
        else:
            new_active.append(trk)
    active_tracks = new_active

    # remaining detections start new tracks
    for j in range(len(cur_pts)):
        if j not in assigned_pts:
            active_tracks.append([[f, cur_pts[j][0], cur_pts[j][1]]])

# add remaining active tracks to finished list
tracks.extend(active_tracks)

# ------------------------------------------------------------------
# 4. Remove very short tracks
filtered_tracks = [t for t in tracks if len(t) >= min_track_len]

# ------------------------------------------------------------------
# 5. Visualize tracks – each track a different colour
cmap = cm.get_cmap("tab20", len(filtered_tracks))
fig, ax = plt.subplots(figsize=(8, 6))
for idx, trk in enumerate(filtered_tracks):
    trk = np.array(trk)                      # shape (n, 3): frame, y, x
    ax.plot(trk[:, 2], trk[:, 1], '-',
            color=cmap(idx), linewidth=2, label=f'Track {idx+1}')
    ax.scatter(trk[:, 2], trk[:, 1], color=cmap(idx), s=30)

ax.set_xlabel('X pixel')
ax.set_ylabel('Y pixel')
ax.set_title('Spot tracks over time')
ax.invert_yaxis()    # image coordinates
ax.set_aspect('equal')
ax.legend(loc='upper right', bbox_to_anchor=(1.15, 1))

# ------------------------------------------------------------------
# 6. Save plot
png_path = "/display_output/tracks.png"
svg_path = "/display_output/tracks.svg"
fig.savefig(png_path, dpi=150, bbox_inches='tight')
fig.savefig(svg_path, format='svg', bbox_inches='tight')
plt.show()

# ------------------------------------------------------------------
# 7. Final outputs
print("Plot of detected tracks (different colours per track) saved.")
print(png_path)
```

# Feedback

### Feedback

**Data Analysis & Image Analysis**
The code performs spot detection and tracking without visualizing intermediate steps. It is crucial to visualize the detected blobs on a few sample frames to ensure the `threshold` and `sigma` parameters are correct and that the adaptive histogram equalization isn't introducing noise.

**Code Quality & Style**
*   **Matplotlib Deprecation:** The use of `cm.get_cmap` is deprecated.
*   **Legend Overload:** The legend is attempting to plot every single track. With many short fragments or noise, the legend becomes massive and covers the plot (as seen in the result). Since tracks are color-coded, a legend for every single track is usually unnecessary.
*   **Algorithm:** The current implementation is a greedy nearest-neighbor approach, not a true Linear Assignment Problem (LAP) solver (which would typically use the Hungarian algorithm via `scipy.optimize.linear_sum_assignment`).

**Code Complexity**
The tracking loop is quite verbose. Using `linear_sum_assignment` would make the code more robust and concise.

#### Improvements

**1. Fix Deprecation and Legend**
```python
# Old code
cmap = cm.get_cmap("tab20", len(filtered_tracks))
# ...
ax.legend(loc='upper right', bbox_to_anchor=(1.15, 1))

# New code
cmap = plt.colormaps['tab20']
# ...
# Remove ax.legend() or only label a few tracks to avoid the massive legend seen in results
```

**2. Implement actual LAP for linking**
```python
# Old code (Greedy)
for i_track in range(D.shape[0]):
    j = np.argmin(D[i_track])
    if D[i_track, j] <= max_dist:
        if j not in assigned_pts:
            # ...

# New code (Linear Assignment Problem)
from scipy.optimize import linear_sum_assignment

# Inside the frame loop:
row_ind, col_ind = linear_sum_assignment(D)
for r, c in zip(row_ind, col_ind):
    if D[r, c] <= max_dist:
        active_tracks[r].append([f, cur_pts[c][0], cur_pts[c][1]])
        assigned_tracks.add(r)
        assigned_pts.add(c)
```

**3. Add Sanity Checks (Intermediate Visualization)**
Before the tracking loop, add a check to ensure spots are correctly detected.
```python
# New addition before tracking
import random
sample_frame = random.randint(0, frames.shape[0]-1)
plt.imshow(frames[sample_frame], cmap='gray')
pts = detect_spots(frames[sample_frame])
plt.scatter(pts[:, 1], pts[:, 0], color='red', s=10)
plt.title(f"Spot detection check: Frame {sample_frame}")
plt.show()
```

**Final Result**
The final output logic is correct, but ensure that the final print is just the filenames as requested.

```python
# Final output block
print(png_path)
print(svg_path)
```

# Your task
Provide the updated code to incorporate the feedback. Also make sure the original task will be fulfilled. Skip all explanations.

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: 10.68s
  • Execution Time: 10.75s
  • Total Time: 125.38s
  • Files:
    • /display_output/notebook_executed.ipynb
    • /display_output/tracks.png
    • /display_output/tracks.svg
LLM backend
TaskFunctionModel
Generate  codeprompt_scadsai_llmopenai/gpt-oss-120b
Fix codeprompt_scadsai_llmopenai/gpt-oss-120b
Determine dependenciesprompt_scadsai_llmopenai/gpt-oss-120b
Generate code  feedbackprompt_scadsai_llmgoogle/gemma-4-31B-it
Summarize codeprompt_scadsai_llmopenai/gpt-oss-120b
Notebook conversionprompt_scadsai_llmopenai/gpt-oss-120b













    


    

    
Execution Output
Tracks visualisation saved as PNG and SVG.
/display_output/tracks_plot.png
# Imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from skimage import io, feature, filters, img_as_float
from scipy.optimize import linear_sum_assignment
from pathlib import Path

# 1. Load timelapse stack
tiff_path = Path("input_data/FakeTracks.tif")
stack = io.imread(tiff_path)                     # (frames, H, W)

# 2. Spot detection (adjusted threshold)
def detect_spots(image, min_sigma=2, max_sigma=4, threshold=0.2):
    img = img_as_float(image)
    img = filters.gaussian(img, sigma=1)
    blobs = feature.blob_log(
        img,
        min_sigma=min_sigma,
        max_sigma=max_sigma,
        num_sigma=5,
        threshold=threshold,
        overlap=0.5,
    )
    return blobs[:, :2]   # (y, x)

# 3. Sanity‑check detection on first frame
fig, ax = plt.subplots()
ax.imshow(stack[0], cmap="gray")
sample_spots = detect_spots(stack[0])
for y, x in sample_spots:
    ax.add_patch(patches.Circle((x, y), radius=3, edgecolor="red", fill=False))
ax.set_title("Spot detection sanity check (frame 0)")
plt.show()

# 4. Detect spots for all frames
detections = []
for f, img in enumerate(stack):
    pts = detect_spots(img)
    df = pd.DataFrame(pts, columns=["y", "x"])
    df["frame"] = f
    detections.append(df)
detections_df = pd.concat(detections, ignore_index=True)

# 5. LAP linking
max_distance = 10
min_track_len = 3
tracks = {}
next_id = 0
active = {}
INF_COST = max_distance * 1000

for f in range(stack.shape[0]):
    cur = detections_df[detections_df["frame"] == f][["y", "x"]].to_numpy()
    if not active:                                   # first frame
        for pt in cur:
            tracks[next_id] = [(f, pt[0], pt[1])]
            active[next_id] = pt
            next_id += 1
        continue

    prev_ids = list(active.keys())
    prev_pos = np.array([active[i] for i in prev_ids])

    if cur.shape[0] == 0:
        active = {}
        continue

    cost = np.linalg.norm(prev_pos[:, None, :] - cur[None, :, :], axis=2)
    cost = np.where(cost > max_distance, INF_COST, cost)
    row_ind, col_ind = linear_sum_assignment(cost)

    assigned_prev, assigned_cur = set(), set()
    for r, c in zip(row_ind, col_ind):
        if cost[r, c] > max_distance:
            continue
        pid = prev_ids[r]
        pt = cur[c]
        tracks[pid].append((f, pt[0], pt[1]))
        active[pid] = pt
        assigned_prev.add(pid)
        assigned_cur.add(c)

    for pid in list(active.keys()):
        if pid not in assigned_prev:
            del active[pid]

    for idx, pt in enumerate(cur):
        if idx not in assigned_cur:
            tracks[next_id] = [(f, pt[0], pt[1])]
            active[next_id] = pt
            next_id += 1

# 6. Remove short tracks
filtered_tracks = {tid: pts for tid, pts in tracks.items() if len(pts) >= min_track_len}

# 7. Visualise tracks
plt.figure(figsize=(8, 6))
cmap = plt.get_cmap("tab10")
for i, (tid, pts) in enumerate(filtered_tracks.items()):
    arr = np.array(pts)
    label = f"Track {tid}" if len(filtered_tracks) < 20 else None
    plt.plot(arr[:, 2], arr[:, 1], "-o", color=cmap(i % 10), label=label)

plt.gca().invert_yaxis()
plt.title("Detected Tracks")
plt.xlabel("X (px)")
plt.ylabel("Y (px)")
if len(filtered_tracks) < 20:
    plt.legend(loc="upper right", fontsize="small")
plt.tight_layout()

# 8. Save figure
out_png = Path("/display_output/tracks_plot.png")
out_svg = Path("/display_output/tracks_plot.svg")
plt.savefig(out_png, dpi=150)
plt.savefig(out_svg)
plt.show()

# 9. Final output
print("Tracks visualisation saved as PNG and SVG.")
print(str(out_png))
You are an expert in python programming. You have a list of framework constraints which you MUST follow.
Your task is to generate a fully functional code snippet that will be used to fulfill the prompt.
Assume your code will be executed in a Jupyter notebook cell.

# Framework constraints
* You may use the following libraries, but only if necessary: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
* When saving files, use the following folder: "/display_output/". Do not create this folder. It exists already.
* pip install is STRICTLY PROHIBITED. You can only use the libraries mentioned above.
* Statistics: When applying statisticals test, ENSURE that pre-conditions for the tests are checked before the tests are performed.
* Final result output (print or display calls): 
  * The second-last print or display call should be a description of the result (e.g. the measurment and a physical unit if relevant).
  * The last print or display call should be the final result ONLY.
  * If the task is to generate a count, ratio or measurements, print the final result using a separate `print` call. 
  * If the task is to answer a yes/no question, print "Yes" or "No" using a separate `print` call. Do not create any JSON for this.
  * If the task is to generate a plot, display the plot.
  * Also plot intermediate results if possible.
* Final result output (file writing):
  * If the task is to generate a text or a string, write the text or string to "/display_output/final_result.txt".
  * If the task is to generate a table, write the table to "/display_output/final_result.csv".
  * If the final result is a plot, display this plot and afterwards, save it in the folder /display_output as .png and as .svg file and print the filename of the .png in the final output of the program.
  * If the task is to generate a number, list, array or dictionary, write the result to "/display_output/final_result.json".
    In that case, do not add additional data structures. Simply json.dump the result to the file. E.g. if the result is x=2, then just do `json.dump(x, fp)`.
  * If the final result was saved as file, make sure to print the filename in the final output of the program.  
* Keep the code short and concise.



Given some task, code to fulfill the task, and detailed feedback, propose new code that incorporates the feedback.
Make sure to keep the code format.

# Task
                
There is a timelapse image dataset in "input_data/FakeTracks.tif". 
Use spot-detection for detecting the bright objects in the individual images. 
Hint: There about 1-6 objects per frame. Hence, there should be a similar number of tracks.
The use the LAP-algorithm to connect the detected dots over time. 
Viualize the tracks. Each track should have a different color. 
Make sure to remove tracks that are very short before visualizing them.
I am expecting a plot of the tracks as final result.

                
# Code

```
# Import required libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from skimage import io, feature, filters, img_as_float
from scipy.optimize import linear_sum_assignment
from pathlib import Path

# ----------------------------------------------------------------------
# 1. Load the timelapse stack
tiff_path = Path("input_data/FakeTracks.tif")
stack = io.imread(tiff_path)                     # shape: (frames, height, width)

# ----------------------------------------------------------------------
# 2. Spot detection per frame (bright blobs)
def detect_spots(image, min_sigma=2, max_sigma=4, threshold=0.02):
    """Return (y, x) coordinates of blobs detected with LoG."""
    # Convert to float and smooth a little
    img = img_as_float(image)
    img = filters.gaussian(img, sigma=1)
    blobs = feature.blob_log(
        img,
        min_sigma=min_sigma,
        max_sigma=max_sigma,
        num_sigma=5,
        threshold=threshold,
        overlap=0.5,
    )
    # blob_log returns (y, x, sigma); keep only y,x
    return blobs[:, :2]

# Detect spots for all frames
detections = []                                   # list of DataFrames per frame
for f, img in enumerate(stack):
    spots = detect_spots(img)
    df = pd.DataFrame(spots, columns=["y", "x"])
    df["frame"] = f
    detections.append(df)

# Concatenate into one DataFrame
detections_df = pd.concat(detections, ignore_index=True)

# ----------------------------------------------------------------------
# 3. Simple LAP linking (linear assignment)
max_distance = 10   # pixels, maximal jump between frames
min_track_len = 3   # minimum number of frames to keep a track

tracks = {}          # track_id -> list of (frame, y, x)
next_id = 0
active = {}          # track_id -> (y, x) position of last known point

# a large finite penalty used for impossible matches
INF_COST = max_distance * 1000

for f in range(stack.shape[0]):
    cur = detections_df[detections_df["frame"] == f][["y", "x"]].to_numpy()
    if len(active) == 0:                     # first frame: start new tracks
        for pt in cur:
            tracks[next_id] = [(f, pt[0], pt[1])]
            active[next_id] = pt
            next_id += 1
        continue

    # Build cost matrix between active tracks and current detections
    prev_ids = list(active.keys())
    prev_pos = np.array([active[i] for i in prev_ids])

    if cur.shape[0] == 0:                     # no detections this frame
        # close all active tracks
        active = {}
        continue

    # Euclidean distance matrix
    cost = np.linalg.norm(prev_pos[:, None, :] - cur[None, :, :], axis=2)

    # Replace distances exceeding max_distance with a large finite cost
    cost = np.where(cost > max_distance, INF_COST, cost)

    # Solve assignment problem
    row_ind, col_ind = linear_sum_assignment(cost)

    assigned_prev = set()
    assigned_cur = set()

    # Keep only assignments that respect the true distance limit
    for r, c in zip(row_ind, col_ind):
        if cost[r, c] > max_distance:
            continue
        pid = prev_ids[r]
        pt = cur[c]
        tracks[pid].append((f, pt[0], pt[1]))
        active[pid] = pt
        assigned_prev.add(pid)
        assigned_cur.add(c)

    # Unmatched previous tracks become inactive (track ends)
    for pid in list(active.keys()):
        if pid not in assigned_prev:
            del active[pid]

    # Unmatched detections start new tracks
    for idx in range(cur.shape[0]):
        if idx not in assigned_cur:
            pt = cur[idx]
            tracks[next_id] = [(f, pt[0], pt[1])]
            active[next_id] = pt
            next_id += 1

# ----------------------------------------------------------------------
# 4. Remove short tracks
filtered_tracks = {
    tid: pts for tid, pts in tracks.items() if len(pts) >= min_track_len
}

# ----------------------------------------------------------------------
# 5. Visualise tracks – each track a different colour
plt.figure(figsize=(8, 6))
cmap = plt.get_cmap("tab10")
for i, (tid, pts) in enumerate(filtered_tracks.items()):
    pts_arr = np.array(pts)
    plt.plot(pts_arr[:, 2], pts_arr[:, 1], "-o", color=cmap(i % 10), label=f"Track {tid}")

plt.gca().invert_yaxis()          # image coordinates: origin top‑left
plt.title("Detected Tracks")
plt.xlabel("X (px)")
plt.ylabel("Y (px)")
plt.legend(loc="upper right", fontsize="small")
plt.tight_layout()

# Save the figure
out_png = Path("/display_output/tracks_plot.png")
out_svg = Path("/display_output/tracks_plot.svg")
plt.savefig(out_png, dpi=150)
plt.savefig(out_svg)

plt.show()

# ----------------------------------------------------------------------
# 6. Output description and final result filename
print("Tracks visualisation saved as a PNG image.")
print(str(out_png))
```

# Feedback

### Feedback

**Data Analysis & Sanity Checks**
The code fails to visualize the input images or the spot detection results before proceeding to the LAP linking. The final plot reveals a massive over-segmentation problem: the task expects 1-6 tracks, but the result contains hundreds. This indicates the `threshold` in `detect_spots` is too low, causing the algorithm to detect noise as spots.

**Code Quality & Complexity**
The LAP implementation is manually handled with a loop over frames. While functional, it is verbose. More importantly, the legend in the final plot is overwhelming because it attempts to list every single (incorrectly detected) track.

**Final Result**
The final output should only print the filenames of the saved plots.

---

### Suggested Improvements

**1. Add Image Visualization and Tuning**
Before linking, display a sample frame with detected blobs to ensure the `threshold` is correct.

**2. Fix Over-segmentation**
Increase the `threshold` for `blob_log` and potentially adjust `min_sigma` to ensure only actual objects are detected.

**3. Improve Plotting**
Remove the legend if there are many tracks, or only plot the legend for the expected number of tracks.

#### Old Code (Spot Detection & Plotting)
```python
def detect_spots(image, min_sigma=2, max_sigma=4, threshold=0.02):
    # ... (blob_log call)
    return blobs[:, :2]

# ... (inside plotting loop)
plt.plot(pts_arr[:, 2], pts_arr[:, 1], "-o", color=cmap(i % 10), label=f"Track {tid}")
# ...
plt.legend(loc="upper right", fontsize="small")
```

#### New Code
```python
# Increase threshold to avoid detecting noise
def detect_spots(image, min_sigma=2, max_sigma=4, threshold=0.2): 
    img = img_as_float(image)
    img = filters.gaussian(img, sigma=1)
    blobs = feature.blob_log(img, min_sigma=min_sigma, max_sigma=max_sigma, 
                             num_sigma=5, threshold=threshold, overlap=0.5)
    return blobs[:, :2]

# --- ADD THIS: Sanity check for detections ---
import matplotlib.patches as patches
fig, ax = plt.subplots()
ax.imshow(stack[0], cmap='gray')
spots_sample = detect_spots(stack[0])
for s in spots_sample:
    ax.add_patch(patches.Circle((s[1], s[0]), 2, color='red', fill=False))
ax.set_title("Spot Detection Sanity Check (Frame 0)")
plt.show()
# ----------------------------------------------

# ... (inside plotting loop)
# Remove label to avoid huge legend if tracking failed, 
# or only label if len(filtered_tracks) < 20
label = f"Track {tid}" if len(filtered_tracks) < 20 else None
plt.plot(pts_arr[:, 2], pts_arr[:, 1], "-o", color=cmap(i % 10), label=label)

if len(filtered_tracks) < 20:
    plt.legend(loc="upper right", fontsize="small")
```

**Final Result Output:**
```python
# Replace the descriptive prints with just the filenames
print(str(out_png))
print(str(out_svg))
```

# Your task
Provide the updated code to incorporate the feedback. Also make sure the original task will be fulfilled. Skip all explanations.

Execution Details
  • Execution reason: Exe
  • Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
  • Final result: [[[255 255 255 255]
  [255 255 255 255]
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  ...
  [255 255 255 255]
  [255 255 255 255]
  [255 255 255 255]]

 [[255 255 255 255]
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  ...
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  [255 255 255 255]]

 [[255 255 255 255]
  [255 255 255 255]
  [255 255 255 255]
  ...
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  [255 255 255 255]
  [255 255 255 255]]

 ...

 [[255 255 255 255]
  [255 255 255 255]
  [255 255 255 255]
  ...
  [255 255 255 255]
  [255 255 255 255]
  [255 255 255 255]]

 [[255 255 255 255]
  [255 255 255 255]
  [255 255 255 255]
  ...
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  [255 255 255 255]
  [255 255 255 255]]

 [[255 255 255 255]
  [255 255 255 255]
  [255 255 255 255]
  ...
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  [255 255 255 255]
  [255 255 255 255]]]
  • Build Time: 0.00s
  • Run Time: 5.34s
  • Execution Time: 5.43s
  • Total Time: 153.11s
  • Files:
    • /display_output/notebook_executed.ipynb
    • /display_output/tracks_plot.png
    • /display_output/tracks_plot.svg
LLM backend
TaskFunctionModel
Generate  codeprompt_scadsai_llmopenai/gpt-oss-120b
Fix codeprompt_scadsai_llmopenai/gpt-oss-120b
Determine dependenciesprompt_scadsai_llmopenai/gpt-oss-120b
Generate code  feedbackprompt_scadsai_llmgoogle/gemma-4-31B-it
Summarize codeprompt_scadsai_llmopenai/gpt-oss-120b
Notebook conversionprompt_scadsai_llmopenai/gpt-oss-120b













    


    

    
Execution Output
Visualization of detected object tracks over time.
/display_output/tracks.png
import numpy as np
import matplotlib.pyplot as plt
from skimage import io, feature
from scipy.optimize import linear_sum_assignment

# ---- Load timelapse ---------------------------------------------------------
tif_path = "input_data/FakeTracks.tif"
frames = io.imread(tif_path)                     # shape: (T, H, W)

# ---- Spot detection per frame (Laplacian of Gaussian) ----------------------
blobs_per_frame = []
for img in frames:
    # set an absolute threshold based on image intensity statistics
    thresh = np.mean(img) + 3 * np.std(img)
    blobs = feature.blob_log(
        img,
        min_sigma=2,
        max_sigma=6,
        num_sigma=5,
        threshold=thresh,
    )
    # keep only (y, x) coordinates
    blobs_per_frame.append(blobs[:, :2])

# ---- Sanity check: visualize detections on first 3 frames ------------------
plt.figure(figsize=(12, 4))
for i in range(min(3, len(frames))):
    plt.subplot(1, 3, i + 1)
    plt.imshow(frames[i], cmap="gray")
    if blobs_per_frame[i].size:
        plt.scatter(blobs_per_frame[i][:, 1], blobs_per_frame[i][:, 0],
                    facecolors="none", edgecolors="r", s=30)
    plt.title(f"Frame {i}")
    plt.axis("off")
plt.tight_layout()
plt.show()

# ---- Simple LAP linking -----------------------------------------------------
max_dist = 10.0                     # max allowed jump between frames
next_track_id = 0
active_tracks = {}                  # track_id -> (y, x, last_frame, points)
finished_tracks = {}

for t, detections in enumerate(blobs_per_frame):
    if t == 0:
        for det in detections:
            active_tracks[next_track_id] = (det[0], det[1], t,
                                            [(t, det[0], det[1])])
            next_track_id += 1
        continue

    track_ids = list(active_tracks.keys())
    last_pos = np.array([[active_tracks[i][0], active_tracks[i][1]]
                         for i in track_ids])

    if len(detections) == 0:
        for tid in track_ids:
            finished_tracks[tid] = active_tracks.pop(tid)[3]
        continue

    cost = np.linalg.norm(last_pos[:, None, :] - detections[None, :, :], axis=2)
    cost[cost > max_dist] = np.inf
    row_ind, col_ind = linear_sum_assignment(cost)

    assigned_tracks, assigned_dets = set(), set()
    for r, c in zip(row_ind, col_ind):
        if cost[r, c] == np.inf:
            continue
        tid = track_ids[r]
        y, x = detections[c]
        points = active_tracks[tid][3]
        points.append((t, y, x))
        active_tracks[tid] = (y, x, t, points)
        assigned_tracks.add(tid)
        assigned_dets.add(c)

    for tid in track_ids:
        if tid not in assigned_tracks:
            finished_tracks[tid] = active_tracks.pop(tid)[3]

    for d_idx, det in enumerate(detections):
        if d_idx not in assigned_dets:
            active_tracks[next_track_id] = (det[0], det[1], t,
                                            [(t, det[0], det[1])])
            next_track_id += 1

# remaining active tracks become finished
for tid, data in active_tracks.items():
    finished_tracks[tid] = data[3]

# ---- Remove very short tracks -----------------------------------------------
min_length = 5          # frames
tracks = [tr for tr in finished_tracks.values() if len(tr) >= min_length]

# ---- Plot tracks (different colour per track) --------------------------------
plt.figure(figsize=(8, 6))
cmap = plt.get_cmap("tab20")
for i, tr in enumerate(tracks):
    tr = np.array(tr)                # (N, 3) → (frame, y, x)
    plt.plot(tr[:, 2], tr[:, 1], '-',
             color=cmap(i % cmap.N), label=f"track {i+1}")

plt.gca().invert_yaxis()
plt.title("Detected object tracks")
plt.xlabel("X pixel")
plt.ylabel("Y pixel")
plt.axis("equal")
plt.tight_layout()

# ---- Save plot ---------------------------------------------------------------
png_path = "/display_output/tracks.png"
svg_path = "/display_output/tracks.svg"
plt.savefig(png_path, dpi=150)
plt.savefig(svg_path)
plt.show()

# ---- Output ------------------------------------------------------------------
print("Visualization of detected object tracks over time.")   # description
print(png_path)                                            # final result filename
You are an expert in python programming. You have a list of framework constraints which you MUST follow.
Your task is to generate a fully functional code snippet that will be used to fulfill the prompt.
Assume your code will be executed in a Jupyter notebook cell.

# Framework constraints
* You may use the following libraries, but only if necessary: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
* When saving files, use the following folder: "/display_output/". Do not create this folder. It exists already.
* pip install is STRICTLY PROHIBITED. You can only use the libraries mentioned above.
* Statistics: When applying statisticals test, ENSURE that pre-conditions for the tests are checked before the tests are performed.
* Final result output (print or display calls): 
  * The second-last print or display call should be a description of the result (e.g. the measurment and a physical unit if relevant).
  * The last print or display call should be the final result ONLY.
  * If the task is to generate a count, ratio or measurements, print the final result using a separate `print` call. 
  * If the task is to answer a yes/no question, print "Yes" or "No" using a separate `print` call. Do not create any JSON for this.
  * If the task is to generate a plot, display the plot.
  * Also plot intermediate results if possible.
* Final result output (file writing):
  * If the task is to generate a text or a string, write the text or string to "/display_output/final_result.txt".
  * If the task is to generate a table, write the table to "/display_output/final_result.csv".
  * If the final result is a plot, display this plot and afterwards, save it in the folder /display_output as .png and as .svg file and print the filename of the .png in the final output of the program.
  * If the task is to generate a number, list, array or dictionary, write the result to "/display_output/final_result.json".
    In that case, do not add additional data structures. Simply json.dump the result to the file. E.g. if the result is x=2, then just do `json.dump(x, fp)`.
  * If the final result was saved as file, make sure to print the filename in the final output of the program.  
* Keep the code short and concise.



Given some task, code to fulfill the task, and detailed feedback, propose new code that incorporates the feedback.
Make sure to keep the code format.

# Task
                
There is a timelapse image dataset in "input_data/FakeTracks.tif". 
Use spot-detection for detecting the bright objects in the individual images. 
Hint: There about 1-6 objects per frame. Hence, there should be a similar number of tracks.
The use the LAP-algorithm to connect the detected dots over time. 
Viualize the tracks. Each track should have a different color. 
Make sure to remove tracks that are very short before visualizing them.
I am expecting a plot of the tracks as final result.

                
# Code

```
import numpy as np
import matplotlib.pyplot as plt
from skimage import io, feature, exposure
from scipy.optimize import linear_sum_assignment
import json
import os

# ---- Load timelapse ---------------------------------------------------------
tif_path = "input_data/FakeTracks.tif"
frames = io.imread(tif_path)                     # shape: (T, H, W)

# ---- Spot detection per frame (Laplacian of Gaussian) ----------------------
# parameters are generous – the dataset has bright, roughly Gaussian blobs
blobs_per_frame = []
for idx, img in enumerate(frames):
    # enhance contrast for more reliable detection
    img_eq = exposure.equalize_adapthist(img)
    blobs = feature.blob_log(
        img_eq,
        min_sigma=2,
        max_sigma=6,
        num_sigma=5,
        threshold=0.02,          # low because we equalized the image
    )
    # blob_log returns (y, x, sigma); keep only y, x
    blobs_per_frame.append(blobs[:, :2])

# ---- Simple LAP linking -----------------------------------------------------
max_dist = 15.0                     # maximum allowed jump between frames
next_track_id = 0
active_tracks = {}                  # track_id -> (last_y, last_x, last_frame)
finished_tracks = {}

for t, detections in enumerate(blobs_per_frame):
    # start new tracks if first frame
    if t == 0:
        for det in detections:
            active_tracks[next_track_id] = (det[0], det[1], t, [(t, det[0], det[1])])
            next_track_id += 1
        continue

    # Build cost matrix between active tracks and current detections
    track_ids = list(active_tracks.keys())
    last_pos = np.array([[active_tracks[i][0], active_tracks[i][1]] for i in track_ids])
    if len(detections) == 0:
        # no detections – all active tracks finish
        for tid in track_ids:
            finished_tracks[tid] = active_tracks.pop(tid)[3]
        continue

    cost = np.linalg.norm(last_pos[:, None, :] - detections[None, :, :], axis=2)

    # Apply distance threshold: set large cost for impossible links
    cost[cost > max_dist] = np.inf

    # Solve assignment
    row_ind, col_ind = linear_sum_assignment(cost)

    assigned_tracks = set()
    assigned_dets = set()

    # Process optimal assignments
    for r, c in zip(row_ind, col_ind):
        if cost[r, c] == np.inf:
            continue                      # too far → treat as unassigned
        tid = track_ids[r]
        y, x = detections[c]
        # extend track
        track = active_tracks[tid][3]
        track.append((t, y, x))
        active_tracks[tid] = (y, x, t, track)
        assigned_tracks.add(tid)
        assigned_dets.add(c)

    # Tracks that were not assigned -> finished
    for tid in track_ids:
        if tid not in assigned_tracks:
            finished_tracks[tid] = active_tracks.pop(tid)[3]

    # Detections that were not assigned -> start new tracks
    for d_idx, det in enumerate(detections):
        if d_idx not in assigned_dets:
            active_tracks[next_track_id] = (det[0], det[1], t,
                                            [(t, det[0], det[1])])
            next_track_id += 1

# Remaining active tracks become finished
for tid, data in active_tracks.items():
    finished_tracks[tid] = data[3]

# ---- Remove very short tracks -----------------------------------------------
min_length = 5                      # frames
tracks = [tr for tr in finished_tracks.values() if len(tr) >= min_length]

# ---- Plot tracks (different colour per track) --------------------------------
plt.figure(figsize=(8, 6))
cmap = plt.get_cmap("tab20")
for i, tr in enumerate(tracks):
    tr = np.array(tr)                # shape (N, 3) → (frame, y, x)
    plt.plot(tr[:, 2], tr[:, 1], '-',
             color=cmap(i % cmap.N), label=f"track {i+1}")

plt.gca().invert_yaxis()            # image origin is top‑left
plt.title("Detected object tracks")
plt.xlabel("X pixel")
plt.ylabel("Y pixel")
plt.axis('equal')
plt.tight_layout()

# ---- Save plot ---------------------------------------------------------------
png_path = "/display_output/tracks.png"
svg_path = "/display_output/tracks.svg"
plt.savefig(png_path, dpi=150)
plt.savefig(svg_path)

# ---- Output ------------------------------------------------------------------
print("Visualization of detected object tracks over time.")   # description
print(png_path)                                            # final result filename
```

# Feedback

### Image Analysis Feedback
The final plot shows a "spaghetti" effect with far too many tracks and erratic movements. The task states there should be only **1-6 objects per frame**. The current result indicates massive over-segmentation (too many false positive detections) and poor tracking (tracks jumping across the image). This is likely caused by `exposure.equalize_adapthist`, which amplifies background noise into "blobs" that the `blob_log` detector then picks up.

### Code Quality & Logic Feedback
1.  **Image Processing**: Adaptive histogram equalization is too aggressive for spot detection in low-signal images. A simple global threshold or using the raw intensity is preferred for Gaussian blobs.
2.  **Parameter Tuning**: The `threshold` in `blob_log` is too low, leading to the detection of noise.
3.  **Data Analysis**: The code does not visualize the detected blobs on the original frames before linking them. This missing sanity check would have revealed the over-segmentation immediately.
4.  **Final Result**: The plot is displayed and saved, but the output print should only be the filename as per requirements.

### Suggested Improvements

**1. Replace Adaptive Equalization and Tune Detection**
Remove `equalize_adapthist` to avoid amplifying noise and increase the `threshold`.

```python
# OLD
# img_eq = exposure.equalize_adapthist(img)
# blobs = feature.blob_log(img_eq, ..., threshold=0.02)

# NEW
# Work with original image or normalized image; increase threshold to filter noise
blobs = feature.blob_log(
    img, 
    min_sigma=2, 
    max_sigma=6, 
    num_sigma=5, 
    threshold=50 # Adjust this value based on the actual intensity of the spots
)
```

**2. Add Sanity Check for Detections**
Visualize the first few frames with detected blobs to ensure only 1-6 objects are found.

```python
# NEW: Sanity check before LAP
plt.figure(figsize=(10, 4))
for i in range(3):
    plt.subplot(1, 3, i+1)
    plt.imshow(frames[i], cmap='gray')
    plt.scatter(blobs_per_frame[i][:, 1], blobs_per_frame[i][:, 0], color='red', s=10)
    plt.title(f"Frame {i}")
plt.show()
```

**3. Refine Output**
Clean up the final print statement to match the requested format.

```python
# OLD
# print("Visualization of detected object tracks over time.")
# print(png_path)

# NEW
print(png_path)
```

**4. LAP Logic**
The current LAP implementation is functional, but since the movement of such objects is usually small, consider reducing `max_dist` once the detections are cleaned up to avoid "jumping" tracks.

# Your task
Provide the updated code to incorporate the feedback. Also make sure the original task will be fulfilled. Skip all explanations.

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: 5.34s
  • Execution Time: 5.43s
  • Total Time: 103.30s
  • Files:
    • /display_output/notebook_executed.ipynb
    • /display_output/tracks.png
    • /display_output/tracks.svg
LLM backend
TaskFunctionModel
Generate  codeprompt_scadsai_llmopenai/gpt-oss-120b
Fix codeprompt_scadsai_llmopenai/gpt-oss-120b
Determine dependenciesprompt_scadsai_llmopenai/gpt-oss-120b
Generate code  feedbackprompt_scadsai_llmgoogle/gemma-4-31B-it
Summarize codeprompt_scadsai_llmopenai/gpt-oss-120b
Notebook conversionprompt_scadsai_llmopenai/gpt-oss-120b