Python API

Kshana提供一流的Python应用程序编程接口（API），基于PyO3和maturin构建。其abi3 wheel针对稳定的Python应用二进制接口（ABI）构建，因此每个平台只需一个wheel即可适用于CPython ≥ 3.9；wheel覆盖Linux、macOS和Windows，每种均提供x86-64与64位ARM版本（见WHEEL_TAGS.md）。随附的类型存根（kshana.pyi＋py.typed）为编辑器及mypy／pyright提供完整的类型信息。

pip install kshana            # from the Python Package Index, PyPI (release wheels)
# or, from a checkout:
pip install maturin && maturin develop --features python

快速入门#

请在仓库检出目录的根目录下运行这些代码片段：它们读取scenarios/下的场景文件。NumPy那一行需要pip install numpy。

import kshana

toml = open("scenarios/clock-holdover.toml").read()

# Typed result with a parsed-dict accessor:
out = kshana.run_typed(toml)
print(out.summary)
data = out.data()                         # a Python dict — no JSON re-parsing
fom = data["quantum"]["fom"]              # figures of merit, quantum side
classical_fom = data["classical"]["fom"]  # … and the classical baseline
print(out.json[:80], "...")               # raw JSON also available
open("chart.svg", "w").write(out.svg)     # the chart SVG

# NumPy arrays straight from the result (NumPy is imported on call; it is not a
# dependency of the package):
curve = out.columns("quantum.adev_curve")  # {"tau_s": ndarray, "adev": ndarray, ...}
adev = curve["adev"]
everything = out.series()                  # every record series, keyed by path

界面#

GNSS IQ与接收机信任度的函数签名#

完整的函数签名，以kshana.pyi所述为准（必需参数之后的每个关键字参数都有默认值；存根中的文档字符串给出各自的含义）。数组请用numpy.asarray(...)包装samples_i／samples_q列表以及i／q输入。

def receiver_trust(toml: str) -> RunOutput: ...

def receiver_trust_replay(session_toml: str, nmea: str | bytes) -> dict: ...
def assess_vessel_log(session_toml: str, log: str | bytes) -> dict: ...
def jammertest_import(catalog_json: str, plans: list[str], log_start_utc: str, location: int | None = None,
                      local_offset_h: float = 2.0, year: int | None = None) -> dict: ...
def jammertest_score(session_toml: str, log: str | bytes, catalog_json: str, plans: list[str],
                     location: int | None = None, local_offset_h: float = 2.0,
                     log_start_utc: str | None = None, year: int | None = None,
                     recover_hold_s: float = 10.0, guard_s: float = 60.0) -> dict: ...
def evidence_create(session_toml: str, log: str | bytes, from_s: float, to_s: float,
                    title: str | None = None, created_utc: str | None = None,
                    seed_hex: str | None = None) -> dict: ...
def evidence_verify(files: dict[str, str | bytes], public_key: str | None = None,
                    full_log: str | bytes | None = None,
                    require_timestamp: bool = False) -> dict: ...
def interference_map(source: str, csv: str, dataset: str, cell_deg: float | None = None,
                     licence: str | None = None, licence_url: str | None = None,
                     attribution: str | None = None, land_geojson: str | None = None) -> list[dict]: ...
def route_exposure(route: str, maps: list[str], date_from: str | None = None,
                   date_to: str | None = None) -> str: ...
def export_sp3(toml: str) -> str: ...
def export_omm(toml: str) -> str: ...
def export_oem(toml: str) -> str: ...
def export_formats(toml: str) -> list[dict]: ...
def export_scenario(toml: str, format: str) -> dict: ...
def import_route(toml: str, geojson: str) -> str: ...
def animate_scenario(toml: str, format: str = "svg", fps: int | None = None,
                     duration_s: float | None = None, width: int | None = None) -> dict: ...
def list_examples(kind: str | None = None) -> dict: ...
def get_example(name: str) -> str: ...
def evidence_attach_timestamp(files: dict[str, str | bytes], token: str | bytes,
                              replace: bool = False) -> dict: ...
def evidence_keygen() -> dict: ...
def iq_sweep(i: list[float], q: list[float], fs_hz: float, signal: str, prns: list[int],
             **options) -> dict: ...
def iq_info(path: str, hash: bool = False) -> dict: ...
def iq_inventory(directory: str, recursive: bool = False, hash: bool = False) -> list[dict]: ...
def bench_export(toml: str, epoch: str | None = None) -> dict: ...
def compliance_report(runs: list[dict[str, str]]) -> dict: ...
def compliance_mapping(sources: bool = False) -> str: ...
def standards_pack_list() -> dict[str, Any]: ...
def standards_pack_run(pack_id: str) -> dict[str, Any]: ...
def lunanet_afs_check_spec() -> dict[str, Any]: ...
def lunanet_afs_check_symbols(symbols: str, max_sp_errors: int = 0) -> dict[str, Any]: ...
def lunanet_afs_generate(frames: int = 1, toi0: int = 0) -> str: ...
def evidence_round_trip(scoring_toml: str, scenario_toml: str, conditions: str, log: str | bytes, from_s: float, to_s: float, title: Optional[str] = None, created_utc: Optional[str] = None, seed_hex: Optional[str] = None) -> dict[str, Any]: ...
def bench_score(truth_json: str, predictions_json: str, threshold: float | None = None) -> str: ...
def bench_leaderboard(results: list[str]) -> str: ...
def osnma_verify(data: bytes | str, format: str = "", start_gst: int | None = None,
                 merkle_root_hex: str = "", public_keys: list[str] | None = None,
                 reference_time: int | None = None, max_time_error: int | None = None) -> dict: ...
def sky_geometry(request: str | dict[str, Any]) -> dict[str, Any]: ...
def sky_monte_carlo(request: str | dict[str, Any], seed: int = 0, n: int = 1000) -> dict[str, Any]: ...
def skyline_presets() -> list[dict[str, Any]]: ...
def skyline_profile(skyline: str | dict[str, Any]) -> dict[str, Any]: ...
def skyplot_svg(request: str | dict[str, Any]) -> str: ...
def trust_telemetry(input: str, format: str, host: str = "", input_kind: str = "jsonl",
                    expose_position: bool = False, syslog_all: bool = False,
                    unix_nano: int = 0) -> str: ...
def nmea_training(toml: str, seed: int | None = None) -> dict[str, str]: ...

def iq_signals() -> list[str]: ...

def iq_scene(
    fs_hz: float,
    duration_s: float,
    signal: str,
    prns: list[int],
    dopplers: Optional[list[float]] = ...,
    cn0_dbhz: Optional[float] = ...,
    center_hz: Optional[float] = ...,
    if_hz: float = ...,
    noise: bool = ...,
    noise_figure_db: float = ...,
    seed: int = ...,
    data: bool = ...,
    threads: int = ...,
    iono_stec: Optional[float] = ...,
    iono_vtec: Optional[float] = ...,
    iono_klobuchar: bool = ...,
    tropo: bool = ...,
    tropo_doy: float = ...,
    s4: Optional[float] = ...,
    scint_tau0: float = ...,
    sigma_phi: float = ...,
    multipath_height: Optional[float] = ...,
    multipath_ground: str = ...,
    land_mobile: bool = ...,
    nlos: bool = ...,
) -> dict[str, Any]: ...

def iq_scene_broadcast(
    fs_hz: float,
    window_s: float,
    nav_text: str,
    rx_lat: float,
    rx_lon: float,
    rx_alt: float,
    prns: Optional[list[int]] = ...,
    start_tow: float = ...,
    cn0_dbhz: Optional[float] = ...,
    center_hz: Optional[float] = ...,
    if_hz: float = ...,
    noise: bool = ...,
    noise_figure_db: float = ...,
    seed: int = ...,
    mask_deg: float = ...,
    threads: int = ...,
) -> dict[str, Any]: ...

def iq_acquire(
    i: list[float],
    q: list[float],
    fs_hz: float,
    signal: str,
    prns: list[int],
    if_hz: float = ...,
    center_hz: Optional[float] = ...,
    coherent: int = ...,
    noncoherent: int = ...,
    doppler_max: float = ...,
    doppler_step: Optional[float] = ...,
    pfa: float = ...,
) -> list[dict[str, Any]]: ...

def iq_acq_surface(
    i: list[float],
    q: list[float],
    fs_hz: float,
    signal: str,
    prn: int,
    if_hz: float = ...,
    center_hz: Optional[float] = ...,
    coherent: int = ...,
    noncoherent: int = ...,
    doppler_max: float = ...,
    doppler_step: Optional[float] = ...,
    pfa: float = ...,
) -> dict[str, Any]: ...

def iq_track(
    i: list[float],
    q: list[float],
    fs_hz: float,
    signal: str,
    prns: list[int],
    if_hz: float = ...,
    center_hz: Optional[float] = ...,
    pll_bw: Optional[float] = ...,
    fll_bw: Optional[float] = ...,
    dll_bw: Optional[float] = ...,
    spacing: Optional[float] = ...,
    coherent: Optional[int] = ...,
    periods_per_bit: Optional[int] = ...,
    acq_coherent: Optional[int] = ...,
    acq_noncoherent: int = ...,
    doppler_max: float = ...,
    max_seconds: Optional[float] = ...,
) -> dict[str, Any]: ...

def iq_frontend(
    i: list[float],
    q: list[float],
    fs_hz: float,
    bandpass_lo: Optional[float] = ...,
    bandpass_hi: Optional[float] = ...,
    bandpass_transition: Optional[float] = ...,
    bandpass_atten: float = ...,
    notch: bool = ...,
    notch_r: float = ...,
    notch_mu: float = ...,
    blank: Optional[float] = ...,
    blank_hold: int = ...,
    excise: bool = ...,
    excise_fft: int = ...,
    excise_pfa: float = ...,
    agc: bool = ...,
    agc_tau: float = ...,
    bits: Optional[int] = ...,
    quant_step: Optional[float] = ...,
    no_agc: bool = ...,
) -> dict[str, Any]: ...

def iq_labfit(toml: str) -> dict[str, Any]: ...
def iq_loop_designs(toml: str) -> list[dict[str, Any]]: ...
def iq_read_epochs(path: str) -> dict[str, Any]: ...
def iq_monitor(path: str, signal: Optional[str] = ..., prns: Optional[list[int]] = ...,
               power: bool = ..., spectral: bool = ..., settings: Optional[str] = ...,
               **options) -> dict[str, Any]: ...
def iq_test_conditions(conditions: str) -> dict[str, Any]: ...
def iq_campaign(campaign: str, out_dir: str, workers: int = 0, resume: bool = True,
                max_cells: Optional[int] = None, dry_run: bool = False) -> dict[str, Any]: ...
def iq_campaign_report(out_dir: str) -> dict[str, Any]: ...

通过Python运行实验室回放测评活动，然后生成其报告（测评活动文件及其录制由你自己提供；已完成的单元在下次调用时会被跳过；iq_read_epochs读取由kshana iq track --epochs运行写出的跟踪历元文件）：

import kshana

summary = kshana.iq_campaign("lab.campaign.toml", "campaign-out", workers=4)
report = kshana.iq_campaign_report("campaign-out")
print(report["digest"])          # None until every cell is done

iq_track的acq_coherent设定交接捕获相干积分多少个码周期。自0.33.0起，其默认值为None，表示自动：约4 ms（对1 ms码如GPS L1 C/A为4个周期，对4 ms或更长的码为1个周期），使通道的起点足够接近，以便频率环牵引入锁；acq_coherent=1则保留0.32的单周期搜索。

类型#

kshana.pyi为检查器（mypy、pyright）给出封闭词汇表的类型：ScenarioKind（所有类别名称，一个Literal）、ErrorKind（error_kind返回的四种标签）、ProvenanceClass，以及TypedDict类型的KindMeta（scenario_kinds()返回其列表）和FieldUnit（结果units块中的一项，它扩展了必需键基类_FieldUnitBase）。它们仅供类型检查器使用，运行时不可导入；tests/typed_api_sync.rs使它们与引擎的列表保持一致。

from typing import TYPE_CHECKING
import kshana
if TYPE_CHECKING:
    from kshana import ScenarioKind

names: "list[ScenarioKind]" = [k["name"] for k in kshana.scenario_kinds()]

RunOutput#

可复现性表#

realtime-frame-eop、lunar-time-budget、lunar-jamming、telecom-timing和leo-navmsg总会发布一张表；moonlight-service-volume在配置了导出站点（export_site_lat_deg＋export_site_lon_deg）时发布一张。其他每个类别都返回csv = None。文本与CLI（命令行界面）写出的<scenario>.table.csv字节完全相同，因此审阅者从wheel复现已发布的表时无需退回到命令行：

import kshana

out = kshana.run_typed(open("scenarios/realtime-frame-eop.toml").read())
if out.csv is not None:
    print(out.write_csv("table.csv"), "bytes written")

结果结构#

性能指标和时间序列位于各运行侧，而不在顶层。对于一次时钟运行，其文档为：

schema_version  engine_version  scenario_hash  seed  threshold_ns  units  figure_tiers
quantum   → spec · series · fom · adev_curve · filter_health
classical → spec · series · fom · adev_curve · filter_health

因此性能指标是data["quantum"]["fom"]，绝不是data["figures_of_merit"]。其他类别输出各自的数据包；包含单位和来源指针的完整逐字段参考是field-units-schema.json，读者指南是SCHEMA.md。

备注#

• validate_toml和error_kind会执行该场景，因此能暴露解析、配置以及运行时错误。它们从不抛出异常。
• 场景无效时，run／run_typed抛出ValueError。
• 结果是可复现的：场景带有其seed，引擎记录一个scenario_hash，因此在同一平台上相同输入产生字节级相同的输出（见REPRODUCIBILITY.md）。
• .array、.columns和.series返回的恰好是.data()所给出的同一批数值的numpy.asarray(...)（相同dtype，逐位一致；tests/python/test_numpy_runoutput.py）。它们在被调用时才导入NumPy，没有NumPy时抛出ImportError；NumPy不属于该包的运行时依赖。KeyError表示路径不存在，ValueError表示该处的值不是数值数组（或不是记录列表）。可运行的notebook位于notebooks/。

不在Python中：长时间运行的进程#

kshana receiver-trust live（带门限和可选--listen TCP服务器的流式进程）、遥测导出器（Prometheus、OTLP、syslog；docs/TRUST-TELEMETRY.md）以及kshana nmea-scenario --tcp/--udp流式传输仅限命令行：绑定在调用返回时即返回，不会保持套接字打开。Python涵盖其中每一项的批处理形式：日志用receiver_trust，流摘录用receiver_trust_replay（给出评分和状态；不应用门限），生成的文本用nmea_training。