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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.

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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, PhotoluminescenceResult, RamanResult, MetrologyResult, or OpticalResult.

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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.

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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.

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Inspect spectroscopy results

For XPS, 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()

# 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()

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Inspect metrology and optical results

Metrology and optical results provide timeseries data from instrument sensors and optical imaging: 
# 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

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Download processed videos

Save annotated MP4 videos to disk: 
client.download_videos(
    data_ids=search_results["Data ID"].to_list(),
    dest_dir="processed/",
)
The files mirror what you see in the web UI.

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Download raw files

To download the original uploaded file instead of processed output: 
client.download_videos(
    data_ids=search_results["Data ID"].to_list(),
    dest_dir="raw/",
    data_type="raw",
)

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Next steps

Stream RHEED

Push frames from your instrument in real time.

Poll Time Series

Monitor streaming analysis updates.