Getting Started

pip install echotime

EchoTime keeps the install surface light, but mixed scientific Python stacks can still produce resolver noise. Use a clean environment when possible, and use the compatibility / doctor flow when you cannot.

• Read data with pandas or NumPy.
• Call one EchoTime function.
• Print a summary card first; export HTML second.

import pandas as pd
from echotime import compare_series

df = pd.read_csv("my_metrics.csv")
report = compare_series(df["sessions"], df["signups"])
print(report.to_summary_card_markdown())

Use this when your question is "does column A move like column B?"

import pandas as pd
from echotime import profile_dataset

df = pd.read_csv("my_timeseries.csv").rename(columns={"date": "timestamp"})
profile = profile_dataset(df, domain="energy")
print(profile.to_summary_card_markdown())

Use this when you have one timestamp column and multiple numeric measurements.

import pandas as pd
from echotime import profile_dataset

df = pd.read_csv("patient_vitals.csv").rename(columns={
    "patient_id": "subject",
    "charttime": "timestamp",
    "lab_name": "channel",
    "lab_value": "value",
})
profile = profile_dataset(df, domain="clinical")
print(profile.to_summary_card_markdown())

Use this when your data are sparse or irregular and live in rows instead of a clean matrix.

# EchoTime similarity summary
overall similarity: ...
top components: shape similarity, trend similarity, spectral similarity

Once the summary card looks sensible, write to_html_report() to disk if you want a shareable artifact.

Preview similarity reports, visuals, and flagship cases without installing Python or starting a server.

Open a starter notebook in a hosted notebook environment.

Run the CLI in an isolated ephemeral environment when packaging allows it.

Run a tiny local web app that turns pasted values into similarity verdicts on your own machine.