Inspect Results

Use client.get() to fetch analysis results for items in the catalogue. The method returns result objects with time series data, extracted frames, pattern graphs, and more.

Fetch analysed items

from atomscale import Client

client = Client(api_key="YOUR_API_KEY")

# Search for completed analyses
search_results = client.search(keywords="demo", data_type="rheed_stationary")

# Get analysis results
analysed = client.get(search_results["Data ID"].to_list())

Each item in analysed is a result object depending on the source data type: RHEEDVideoResult, RHEEDImageResult, XPSResult, XRDResult, PhotoluminescenceResult, RamanResult, MetrologyResult, OpticalResult, or EllipsometryResult. Anything the SDK does not recognize comes back as UnknownResult with the raw catalogue entry attached.

Inspect time series data

For video results, access the timeseries_data attribute to get a DataFrame with per-frame metrics:

video_item = analysed[0]
timeseries = video_item.timeseries_data

print(timeseries.columns)
print(timeseries.tail())

The timeseries frame contains specular intensity, strain metrics, cluster IDs, and other summary features for every frame in the video.

get() returns the standard feature set. For the full low-level feature space, per-frame segmentation masks, or a time window, use get_rheed_timeseries() instead.

Work with extracted frames

Video results include snapshot images at key moments:

snapshot = video_item.snapshot_image_data[0]

# Get a matplotlib figure
figure = snapshot.get_plot()

# Access the detected diffraction pattern as a NetworkX graph
fingerprint = snapshot.pattern_graph

# Get a tidy table describing each spot
df = snapshot.get_pattern_dataframe()

The pattern_graph exposes the detected diffraction network, while get_pattern_dataframe() returns positions, intensities, and labels for each spot.

Inspect spectroscopy results

For XPS, XRD, photoluminescence, and Raman results, access the spectral data and generate plots:

# XPS
xps_result = analysed[0]
print(xps_result.predicted_composition)   # dict of element → fraction
print(xps_result.detected_peaks)
figure = xps_result.get_plot()

# XRD
xrd_result = analysed[0]
print(xrd_result.two_theta)
print(xrd_result.intensities)
print(xrd_result.wavelength_angstrom)     # Cu Kα by default
figure = xrd_result.get_plot()

# Photoluminescence
pl_result = analysed[0]
print(pl_result.energies)
print(pl_result.intensities)
figure = pl_result.get_plot()

# Raman
raman_result = analysed[0]
print(raman_result.raman_shift)
print(raman_result.intensities)
figure = raman_result.get_plot()

XRD peaks are dicts with two_theta, intensity, d_spacing_angstrom, prominence, and fwhm_degrees.

Inspect metrology, optical, and ellipsometry results

These result types carry timeseries data from instrument sensors, optical imaging, and ellipsometry fits:

# Metrology (pyrometer, pressure, etc.)
metrology_result = analysed[0]
print(metrology_result.timeseries_data.columns)
print(metrology_result.timeseries_data.tail())

# Optical imaging
optical_result = analysed[0]
print(optical_result.timeseries_data.columns)  # Edge Perimeter, Circularity, etc.

# Access optical snapshots
if optical_result.snapshot_image_data:
    snapshot = optical_result.snapshot_image_data[0]
    snapshot.processed_image.show()  # PIL Image

# Ellipsometry
ellipsometry_result = analysed[0]
df = ellipsometry_result.timeseries_data
print(df.filter(like="psi_").columns)   # per-wavelength channels
print(df["thickness"].tail())           # scalar fits

Fetch detected changepoints

get_changepoints() returns the changepoints the platform detected in a run, so you can flag transitions without re-analysing the timeseries yourself:

changepoints = client.get_changepoints(
    data_ids=search_results["Data ID"].to_list(),
    detection_method="intensity_profile",
    severity="critical",
)

print(changepoints[["data_id", "property_name", "score", "window_start_elapsed"]])

By default this returns only critical changepoints from the latest intensity_profile run. Pass None to either filter to widen the search, latest_only=False for every historical run, or as_dataframe=False for ChangepointResult objects.

See Changepoint Detection for how the detection methods differ.

Download processed files

Save processed output to disk. download() works for every data type the platform stores, not only video:

client.download(
    data_ids=search_results["Data ID"].to_list(),
    dest_dir="processed/",
)

The files mirror what you see in the web UI.

Download raw files

To download the original uploaded file instead of processed output:

client.download(
    data_ids=search_results["Data ID"].to_list(),
    dest_dir="raw/",
    data_type="raw",
)

download_videos() is a deprecated alias for download(). It still works, but use download() in new code.

Next steps

RHEED Features & Masks

Query low-level features and segmentation masks.

Similarity & Embeddings

Compare growths with embedding vectors.

Stream RHEED

Push frames from your instrument in real time.

Poll Time Series

Monitor streaming analysis updates.

Introduction

Guides

References

Fetch analysed items

Inspect time series data

Work with extracted frames

Inspect spectroscopy results

Inspect metrology, optical, and ellipsometry results

Fetch detected changepoints

Download processed files

Download raw files

Next steps

RHEED Features & Masks

Similarity & Embeddings

Stream RHEED

Poll Time Series

​Fetch analysed items

​Inspect time series data

​Work with extracted frames

​Inspect spectroscopy results

​Inspect metrology, optical, and ellipsometry results

​Fetch detected changepoints

​Download processed files

​Download raw files

​Next steps

RHEED Features & Masks

Similarity & Embeddings

Stream RHEED

Poll Time Series

Fetch analysed items

Inspect time series data

Work with extracted frames

Inspect spectroscopy results

Inspect metrology, optical, and ellipsometry results

Fetch detected changepoints

Download processed files

Download raw files

Next steps