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map_causal_chains maps the trajectory of directional signals as they emerge at the microscale and propagate through a hierarchy of increasing timeframes. By identifying the origin of a change, it constructs causal chains to quantify how micro-scale shifts evolve into structural change across expanding time scales.

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Function Parameters

ParameterTypeRequiredDescription
data_inputlist[tuple]TrueA list of tuples containing the source (URL or local path) and the associated timeframe label (e.g. (‘path/to/file.csv’, ‘1m’).

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Supported Time Resolutions

UnitSymbolSeconds (ss)
Planck Timept5.39×10−445.39 \times 10^{-44}
Yoctosecondys10−2410^{-24}
Zeptosecondzs10−2110^{-21}
Attosecondas10−1810^{-18}
Femtosecondfs10−1510^{-15}
Picosecondps10−1210^{-12}
Nanosecondns10−910^{-9}
Microsecondus10−610^{-6}
Millisecondms10−310^{-3}
Seconds11
Minutem6060
Hourh3,6003,600
Dayd86,40086,400
Weekw604,800604,800

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Python Code Example

import sumtyme 

# Initialise the sumtyme client with the provided API key from your dashboard
client = sumtyme.client(apikey='your-api-key-here')

api_outputs = [
    ("https://raw.githubusercontent.com/sumteam/data_store/main/GLD/api_outputs/GLD_1s_reactive_outputs.csv", '1s'),
    ("https://raw.githubusercontent.com/sumteam/data_store/main/GLD/api_outputs/GLD_5s_reactive_outputs.csv", '5s'),
    ("https://raw.githubusercontent.com/sumteam/data_store/main/GLD/api_outputs/GLD_15s_reactive_outputs.csv", '15s'),
    ("https://raw.githubusercontent.com/sumteam/data_store/main/GLD/api_outputs/GLD_30s_reactive_outputs.csv", '30s'),
    ("https://raw.githubusercontent.com/sumteam/data_store/main/GLD/api_outputs/GLD_1m_reactive_outputs.csv", '1m'),
    ("https://raw.githubusercontent.com/sumteam/data_store/main/GLD/api_outputs/GLD_2m_reactive_outputs.csv", '2m'),
    ("https://raw.githubusercontent.com/sumteam/data_store/main/GLD/api_outputs/GLD_5m_reactive_outputs.csv", '5m'),
    ("https://raw.githubusercontent.com/sumteam/data_store/main/GLD/api_outputs/GLD_10m_reactive_outputs.csv", '10m'),
]


# Execute Causal Mapping
# initial_chain_starts: Specific datetime where a chain first starts based on observed timeframes
# causal_chain_details: A detailed breakdown of how the signal moved across different timeframes.
initial_chain_starts, causal_chain_details = client.map_causal_chains(api_outputs=scales)

Output results for review
print("--- Chain Inception Points ---")
print(initial_chain_starts)

print("\n--- Detailed Causal Path Analysis ---")
print(causal_chain_details])