Inspect Results

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())

video_item = analysed[0]
timeseries = video_item.timeseries_data

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

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()

# 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()

# 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

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

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