addereq-dm

Dameng-backed earthquake precursor time-series data access, processing, and plotting SDK. This package is the API-based successor to the Oracle-oriented addereq package.

Install

Python 3.9 or newer is required.

Install the package with plotting support. The distribution name is addereq-dm and the Python import name is addereq_dm:

pip install "addereq-dm[viz]"

Connection config

For CLI usage, the recommended place for credentials is a user-level config file instead of the project directory.

Default config file location:

• Windows: %APPDATA%\\addereq-dm\\config.env
• Linux/macOS: ~/.config/addereq-dm/config.env

Initialize the config from CLI with your own values:

addereq-dm init-config \
  --base-url http://your-api-server:8080 \
  --app-id your_app_id \
  --secret your_secret

If any required value is omitted, the CLI will prompt for it interactively. timeout defaults to 10.

Or initialize from Python:

from addereq_dm import initialize_user_config

initialize_user_config(
    base_url="http://your-api-server:8080",
    app_id="your_app_id",
    secret="your_secret",
)

Example written file:

DM_API_BASE_URL="http://your-api-server:8080"
DM_API_APP_ID="your_app_id"
DM_API_SECRET="your_secret"
DM_API_TIMEOUT="10"

The CLI resolves values in this order:

• command-line arguments
• --env-file if provided
• the default user config file above
• current process environment variables
• built-in defaults such as DM_API_TIMEOUT=10

Business parameters such as station, point, item, sample rate, and time range are still expected to be passed explicitly in code or CLI commands.

Quick start

from addereq_dm import create_geophysics_client

api = create_geophysics_client(
    base_url="http://your-api-server:8080",
    app_id="your_app_id",
    secret="your_secret",
)

Core fetch API

df = api.ts.fetch_dys(
    station="taian_center",
    point="1",
    item="vertical_z",
    source_sample_rate="02",
    output_sample_rate="02",
    start_time="2025-11-01 00:00:00",
    end_time="2025-11-03 00:00:00",
)

The Python API uses explicit names for the two sample-rate concepts:

• source_sample_rate: raw input sample rate
• output_sample_rate: output sample rate for the returned series

For the CLI, use the corresponding options --source-sample-rate and --output-sample-rate. The configuration variables are DM_SOURCE_SAMPLE_RATE and DM_OUTPUT_SAMPLE_RATE.

Accepted sample-rate aliases currently include:

• 01, minute, min
• 02, second, sec
• 60, hour, h
• 90, day, d
• common Chinese aliases are also supported in code

Supported input styles:

• station: station id or station name
• point: point id, point number, or point name; may be omitted
• item: item id or item name

When resolution fails or the query is ambiguous, the resolver returns candidate suggestions to help narrow the scope.

Long-range requests are handled automatically. When the requested range spans more than 30 natural days, the SDK splits the request into multiple upstream calls, merges the returned frames, sorts by timestamp, and deduplicates boundary rows. Chunk boundaries follow an exclusive end_time rule, so each chunk covers [start_time, end_time) and the next chunk starts exactly at the previous chunk's end_time.

Use the next midnight when requesting a complete final day. For example, data for November 1-2 should use end_time="2025-11-03 00:00:00".

Scope keywords

The following keywords are supported for broad queries:

• "all"
• "*"
• common Chinese equivalents for “all” are also supported in code

Lightweight processing

stats = api.ts.summarize(df)
report = api.ts.quality_report(df)
hourly = api.ts.resample(df, "1h")
centered = api.ts.center(hourly)
detrended = api.ts.detrend(df, method="mean")
cleaned = api.ts.clip_outliers(df, zscore=3.0)
differenced = api.ts.difference(df)
robust = api.ts.hampel(df, window_size=15, threshold=5.0)
complete = api.ts.expand_timeline(df)
aligned = api.ts.align([df1, df2], labels=["station_a", "station_b"])

The processing layer normalizes sentinel missing values such as 999999 into real missing values before summaries, quality checks, resampling, alignment, and plotting.

Frames containing multiple logical series are processed independently by STATIONID, POINTID, and ITEMID. Multi-series summaries and quality reports include a series list with per-series results. Pass group_cols=[] only when an intentional whole-frame calculation is required.

Normalized output uses SAMPLERATE for the actual returned series rate and SOURCE_SAMPLERATE for the upstream source rate. VALUE is the canonical value column; OBSVALUE remains as a compatibility mirror for existing addereq workflows.

expand_timeline uses the sample rate to insert timestamps omitted by the API. MISSING_REASON="missing_record" identifies inserted rows, while MISSING_REASON="sentinel" identifies explicit upstream values such as 999999.

Optional metadata snapshot

Online resolution and plotting use live API metadata plus process-local memory caching. They do not automatically read or write persistent metadata. If offline plotting is required, explicitly create a station, point, item, and unit snapshot under the same user configuration directory as config.env.

Snapshot namespaces are SHA-256 hashes of the normalized API base URL; credentials are never written into snapshot files or keys. Refresh failures are reported directly and never hidden by silently using old metadata.

addereq-dm metadata path
addereq-dm metadata status
addereq-dm metadata refresh
addereq-dm metadata refresh --station-id 37001
addereq-dm metadata clear

Optional batch fetch report

df, report = api.ts.fetch_dys_with_report(
    station="all",
    point="1",
    item="3123",
    source_sample_rate="02",
    output_sample_rate="02",
    start_time="2025-11-01 00:00:00",
    end_time="2025-11-03 00:00:00",
    max_targets=20,
    allow_partial=True,
)

report contains:

• a local request identifier for tracing one aggregated SDK fetch
• total elapsed time and elapsed time per target
• target count
• success count
• failure count
• successful targets
• failed targets with error summaries
• requested chunk count
• successful chunk count
• failed chunk count and failed time ranges

With allow_partial=True, successful chunks remain available when another chunk for the same target fails. The failures remain visible in report.

Plotting

Plot grouped by item:

api.plot.plot_by_items(
    df,
    prefix="demo_",
    fig_label="_item",
    show_mean=True,
    overlay_earthquakes=True,
    xlabel="Time",
)

Plot grouped by station and point:

api.plot.plot_by_stations(
    df,
    prefix="demo_",
    fig_label="_station",
    show_mean=False,
    overlay_earthquakes=False,
    ylabel="Displacement",
)

Plotting dependencies are loaded lazily, so importing the package without matplotlib is still supported when plotting is not used.

Subplot ordering rules are stable:

• plot_by_items sorts station subplots by STATIONID
• plot_by_stations sorts item subplots by ITEMID

CLI

Initialize user config:

addereq-dm init-config --base-url http://your-api-server:8080 --app-id your_app_id --secret your_secret

Resolve station, point, or item:

addereq-dm resolve --station taian_center --point 1 --item vertical_z

Fetch data and export to file:

addereq-dm fetch \
  --station taian_center \
  --point 1 \
  --item 3123 \
  --start-time "2025-01-01 00:00:00" \
  --end-time "2025-01-01 01:00:00" \
  --kind dys \
  --allow-partial \
  --workers 2 \
  --out data.csv \
  --summary

CSV, JSON, and Parquet exports include a <data-file>.metadata.json sidecar with the query, data kind, labels, units, and fetch report. The SDK uses this sidecar automatically when the file is imported or plotted locally:

from addereq_dm import export_timeseries, import_timeseries

export_timeseries(df, "data.parquet", metadata={"project": "weekly-review"})
restored = import_timeseries("data.parquet")

--workers enables controlled concurrency across independent resolved targets. It defaults to 1 and is capped at 8; each target's 30-day windows remain sequential, and merged output keeps target order deterministic.

Plot an existing file:

addereq-dm plot --input data.csv --output-dir figures

Use an explicit persistent metadata snapshot instead of the export sidecar:

addereq-dm plot --input data.csv --offline-metadata --output-dir figures

Local files exported by the SDK restore their embedded labels and units automatically. --offline-metadata explicitly replaces those labels with the persistent snapshot and is the only plotting mode that reads that snapshot.

Inspect a local file without API configuration:

addereq-dm quality --input data.csv

Fetch and plot directly:

addereq-dm plot \
  --station taian_center \
  --point 1 \
  --item 3123 \
  --start-time "2025-01-01 00:00:00" \
  --end-time "2025-01-01 01:00:00" \
  --kind dys \
  --output-dir figures

Use --kind dyu for processed data. CP is intentionally not exposed until its upstream API is stable.

See ROADMAP.md for compatibility, rate limiting, and reporting work.