OMM interface overhaul: full-fidelity dicts, file/text loaders, TLE conversion, one parser for Rust and Python

CHANGELOG.md

@@ -4,6 +4,10 @@ Only recent releases are listed. Older entries are in this file's git history (`
 
 ## Unreleased
 
+### Changed
+
+- OMM interface overhaul: `omm_from_url` returns every field the source provided (metadata, `COMMENT`, Space-Track extras such as `OBJECT_TYPE`/`RCS_SIZE`/`TLE_LINE1`, XML `USER_DEFINED` parameters) instead of 17 keys; new `omm_from_file` / `omm_from_text` loaders (JSON or XML, detected from content) replace the `xmltodict` workaround; `TLE.from_omm` / `TLE.to_omm` convert between the representations; `sgp4` dict inputs go through the same serde parser as Rust (quoted numbers, `null`/empty optionals, case-insensitive metadata, any level of an xmltodict tree, `EPOCH` as `time`/`datetime`); propagation rejects `EPHEMERIS_TYPE` 4 (SGP4-XP) instead of running classic SGP4 on it; stubs gain the `OMMDict` type. Rust: `OMM.epoch` is an `Instant` (was a string; `epoch_instant()` deprecated), `OMM` derives `Serialize`, gains `from_mean_elements`, `from_tle`/`to_tle`, `from_json_value`, `from_text`/`from_file`, `reset_cache`; `from_json_string` accepts a bare object; `omm::Error` is `#[non_exhaustive]` with a single `InvalidField` variant; `Default` is removed ([#173](https://github.com/ssmichael1/satkit/pull/173))
+
 ### Fixed
 
 - `satkit.density.nrlmsise(altitude_m, latitude_rad, longitude_rad, time)` converts its radian latitude/longitude to the degrees the model takes; the values were passed through unchanged, so a caller following the stub at 60° N was evaluated at 1.05° N (the `itrfcoord` overload and `nrlmsise00(latitude_deg=...)` were already correct) ([#171](https://github.com/ssmichael1/satkit/pull/171))

docs/api/tle.md

@@ -4,10 +4,16 @@ API reference for the SGP4 analytic propagator and its inputs. For background, s
 
 - [TLEs, SGP4 & OMMs](../guide/tle.md) — theory and format descriptions
 - Primary sources: [Hoots & Roehrich (1980)](../guide/references.md#hoots1980) and [Vallado et al. (2006)](../guide/references.md#vallado2006) for SGP4; [Vallado & Crawford (2008)](../guide/references.md#vallado2008) for TLE fitting; [CCSDS 502.0-B-3](../guide/references.md#ccsds502) for OMMs
-- Tutorials: [Two-Line Element Set](../tutorials/Two-Line Element Set.ipynb), [TLE Fitting](../tutorials/TLE Fitting.ipynb), [SGP4 vs Numerical Propagation](../tutorials/SGP4 vs Numerical Propagation.ipynb)
+- Tutorials: [Two-Line Element Set](../tutorials/Two-Line Element Set.ipynb), [Orbital Mean-Element Message](../tutorials/Orbital Mean-Element Message.ipynb), [TLE Fitting](../tutorials/TLE Fitting.ipynb), [SGP4 vs Numerical Propagation](../tutorials/SGP4 vs Numerical Propagation.ipynb)
 
 ::: satkit.TLE
 
 ::: satkit.sgp4
 
 ::: satkit.omm_from_url
+
+::: satkit.omm_from_file
+
+::: satkit.omm_from_text
+
+::: satkit.OMMDict

docs/guide/tle.md

@@ -39,9 +39,11 @@ OMMs are commonly published as:
 - XML
 - KVN (key-value notation)
 
-OMMs are commonly published as JSON or XML. The `satkit.omm_from_url()` function fetches OMMs from a URL and auto-detects the format, returning a list of Python dictionaries that can be passed directly to `satkit.sgp4()`.
+satkit reads the JSON and XML forms (KVN is not supported). In Python an OMM is a plain dictionary keyed by the CCSDS field names: `satkit.omm_from_url()`, `satkit.omm_from_file()` and `satkit.omm_from_text()` return a list of them, with every field the source provided (Space-Track's catalog extras such as `OBJECT_TYPE` and `RCS_SIZE` included) and numbers converted from Space-Track's quoted strings. `satkit.sgp4()` accepts these dictionaries directly, as well as the raw output of `json.load` on a CelesTrak or Space-Track response. `satkit.TLE.from_omm()` and `satkit.TLE.to_omm()` convert between the two representations.
 
-You can also provide OMM dictionaries manually (e.g. parsed from a local JSON file).
+A message whose `MEAN_ELEMENT_THEORY` is not SGP4, whose `TIME_SYSTEM` is not UTC, or whose `EPHEMERIS_TYPE` is 4 (SGP4-XP, which Space-Track distributes alongside classic SGP4 sets) is rejected rather than propagated with the wrong theory.
+
+In Rust the same message is the `satkit::omm::OMM` struct, which implements `SGP4Source`, serializes back to JSON with `serde`, and converts with `OMM::from_tle` / `OMM::to_tle`.
 
 ## Loading from URLs
 
@@ -132,3 +134,20 @@ XML format works the same way -- just change the URL:
 ```python
 omms = sk.omm_from_url("https://celestrak.org/NORAD/elements/gp.php?GROUP=stations&FORMAT=xml")
 ```
+
+### OMM from a local file, and conversion to a TLE
+
+```python
+import satkit as sk
+
+# JSON or XML, detected from the content; one dict per message
+omms = sk.omm_from_file("gp.json")
+
+# Everything the source provided is in the dict
+print(omms[0]["OBJECT_NAME"], omms[0].get("OBJECT_TYPE"), omms[0].get("RCS_SIZE"))
+
+# The same element set as a TLE object, and back again
+tle = sk.TLE.from_omm(omms[0])
+print("\n".join(tle.to_2line()))
+assert sk.TLE.from_omm(tle.to_omm()).to_2line() == tle.to_2line()
+```

docs/requirements.txt

@@ -15,7 +15,6 @@ scienceplots
 cartopy
 certifi
 ipykernel
-xmltodict
 pandas
 scipy
 requests

docs/tutorials/Orbital Mean-Element Message.ipynb

@@ -98,69 +98,7 @@
    "id": "4",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import satkit as sk\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import scienceplots  # noqa: F401\n",
-    "plt.style.use([\"science\", \"no-latex\", \"../satkit.mplstyle\"])\n",
-    "%config InlineBackend.figure_formats = ['svg']\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\", \"Downloading\")\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "\n",
-    "# Fetch the latest ISS ephemeris in XML format.  sk.omm_from_url auto-detects\n",
-    "# the format and normalizes the result to the same list-of-dicts shape as the\n",
-    "# JSON example above — no xmltodict or hand-written tree walking needed.\n",
-    "url = \"https://celestrak.org/NORAD/elements/gp.php?CATNR=25544&FORMAT=xml\"\n",
-    "try:\n",
-    "    omm = sk.omm_from_url(url)[0]\n",
-    "except RuntimeError as e:\n",
-    "    print(f\"Live fetch unavailable ({e}); using the pinned OMM\")\n",
-    "    omm = ISS_OMM\n",
-    "\n",
-    "# Get a representative time from the OMM epoch\n",
-    "epoch = sk.time(omm[\"EPOCH\"])\n",
-    "# Time array spanning roughly one orbit at 1-minute cadence\n",
-    "time_array = [epoch + sk.duration(minutes=i) for i in range(97)]\n",
-    "\n",
-    "# TEME (inertial) output from SGP4\n",
-    "pTEME, _vTEME = sk.sgp4(omm, time_array)\n",
-    "\n",
-    "# Rotate to Earth-fixed\n",
-    "pITRF = [sk.frametransform.rotation(sk.frame.TEME, sk.frame.ITRF, t) * p for t, p in zip(time_array, pTEME)]\n",
-    "\n",
-    "coord = [sk.itrfcoord(x) for x in pITRF]\n",
-    "\n",
-    "lat, lon, alt = zip(*[(c.latitude_deg, c.longitude_deg, c.altitude) for c in coord])\n",
-    "\n",
-    "# Break ground track at longitude discontinuities (date line crossings)\n",
-    "lon_arr, lat_arr = np.array(lon), np.array(lat)\n",
-    "breaks = np.where(np.abs(np.diff(lon_arr)) > 180)[0] + 1\n",
-    "lon_segs = np.split(lon_arr, breaks)\n",
-    "lat_segs = np.split(lat_arr, breaks)\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(10, 5), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
-    "ax.add_feature(cfeature.LAND, facecolor=\"lightgray\")\n",
-    "ax.add_feature(cfeature.BORDERS, linewidth=0.5)\n",
-    "ax.add_feature(cfeature.COASTLINE, linewidth=0.5)\n",
-    "for lo, la in zip(lon_segs, lat_segs):\n",
-    "    ax.plot(lo, la, linewidth=1.5, color=\"C0\", transform=ccrs.PlateCarree())\n",
-    "ax.set_title(\"ISS Ground Track\")\n",
-    "ax.set_global()\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(10, 4))\n",
-    "ax.plot([t.datetime() for t in time_array], np.array(alt) / 1e3)\n",
-    "ax.set_xlabel(\"Time\")\n",
-    "ax.set_ylabel(\"Altitude (km)\")\n",
-    "ax.set_title(\"ISS Altitude vs Time\")\n",
-    "fig.autofmt_xdate()\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
+   "source": "import json\nimport tempfile\nimport os\n\n# --- JSON file -----------------------------------------------------------\n# Write a small single-satellite OMM JSON file, standing in for a\n# Space-Track / CelesTrak bulk download saved to disk.\nomm_json = \"\"\"[{\n  \"OBJECT_NAME\": \"ISS (ZARYA)\", \"OBJECT_ID\": \"1998-067A\",\n  \"EPOCH\": \"2021-10-02T14:10:59.999\", \"TIME_SYSTEM\": \"UTC\",\n  \"MEAN_ELEMENT_THEORY\": \"SGP4\",\n  \"MEAN_MOTION\": \"15.48915330\", \"ECCENTRICITY\": \"0.0007417\",\n  \"INCLINATION\": \"51.6432\", \"RA_OF_ASC_NODE\": \"351.4697\",\n  \"ARG_OF_PERICENTER\": \"130.5364\", \"MEAN_ANOMALY\": \"329.6482\",\n  \"BSTAR\": \"0.0001027\", \"MEAN_MOTION_DOT\": \"0.00016717\",\n  \"MEAN_MOTION_DDOT\": \"0\"\n}]\"\"\"\nwith tempfile.NamedTemporaryFile(\"w\", suffix=\".json\", delete=False) as fh:\n    fh.write(omm_json)\n    json_path = fh.name\n\n# sk.omm_from_file detects JSON vs XML from the content and returns the\n# same list of dicts as sk.omm_from_url. (Plain json.load works too: sgp4\n# accepts the raw dict, quoted numbers and all.)\nomm_list = sk.omm_from_file(json_path)\nos.unlink(json_path)\n\nepoch = sk.time(omm_list[0][\"EPOCH\"])\np_json, v_json = sk.sgp4(omm_list[0], epoch + sk.duration(minutes=30))\nprint(f\"Position from JSON file (m): {p_json}\")\n\n# --- XML text ------------------------------------------------------------\n# The same OMM in CCSDS XML. sk.omm_from_text parses it directly; no XML\n# library or tree-walking needed.\nomm_xml = \"\"\"<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<ndm><omm id=\"CCSDS_OMM_VERS\" version=\"2.0\">\n  <body><segment>\n    <metadata>\n      <OBJECT_NAME>ISS (ZARYA)</OBJECT_NAME><OBJECT_ID>1998-067A</OBJECT_ID>\n      <CENTER_NAME>EARTH</CENTER_NAME><REF_FRAME>TEME</REF_FRAME>\n      <TIME_SYSTEM>UTC</TIME_SYSTEM>\n      <MEAN_ELEMENT_THEORY>SGP4</MEAN_ELEMENT_THEORY>\n    </metadata>\n    <data>\n      <meanElements>\n        <EPOCH>2021-10-02T14:10:59.999</EPOCH>\n        <MEAN_MOTION>15.48915330</MEAN_MOTION>\n        <ECCENTRICITY>0.0007417</ECCENTRICITY>\n        <INCLINATION>51.6432</INCLINATION>\n        <RA_OF_ASC_NODE>351.4697</RA_OF_ASC_NODE>\n        <ARG_OF_PERICENTER>130.5364</ARG_OF_PERICENTER>\n        <MEAN_ANOMALY>329.6482</MEAN_ANOMALY>\n      </meanElements>\n      <tleParameters>\n        <EPHEMERIS_TYPE>0</EPHEMERIS_TYPE>\n        <CLASSIFICATION_TYPE>U</CLASSIFICATION_TYPE>\n        <NORAD_CAT_ID>25544</NORAD_CAT_ID>\n        <ELEMENT_SET_NO>900</ELEMENT_SET_NO>\n        <REV_AT_EPOCH>29935</REV_AT_EPOCH>\n        <BSTAR>0.0001027</BSTAR>\n        <MEAN_MOTION_DOT>0.00016717</MEAN_MOTION_DOT>\n        <MEAN_MOTION_DDOT>0</MEAN_MOTION_DDOT>\n      </tleParameters>\n    </data>\n  </segment></body>\n</omm></ndm>\"\"\"\n\nomm_from_xml = sk.omm_from_text(omm_xml)[0]\np_xml, v_xml = sk.sgp4(omm_from_xml, epoch + sk.duration(minutes=30))\nprint(f\"Position from XML text  (m): {p_xml}\")\n\n# Same OMM, same epoch -> identical states from both formats\nassert (p_json == p_xml).all() and (v_json == v_xml).all()\nprint(\"JSON and XML routes agree.\")\n\n# --- TLE <-> OMM -----------------------------------------------------------\n# An OMM dict converts to a TLE object and back; the TLE lines are the\n# classic 69-character form of the same element set.\ntle = sk.TLE.from_omm(omm_from_xml)\nprint(\"\\n\".join(tle.to_2line()))\nprint(json.dumps(tle.to_omm(), indent=1)[:200], \"...\")\n"
   },
   {
    "cell_type": "markdown",
@@ -171,7 +109,7 @@
   {
    "cell_type": "code",
    "id": "7c9e6f6c",
-   "source": "import json\nimport tempfile\nimport os\nimport xmltodict\n\n# --- JSON file -----------------------------------------------------------\n# Write a small single-satellite OMM JSON file, standing in for a\n# Space-Track / CelesTrak bulk download saved to disk.\nomm_json = \"\"\"[{\n  \"OBJECT_NAME\": \"ISS (ZARYA)\", \"OBJECT_ID\": \"1998-067A\",\n  \"EPOCH\": \"2021-10-02T14:10:59.999\", \"TIME_SYSTEM\": \"UTC\",\n  \"MEAN_ELEMENT_THEORY\": \"SGP4\",\n  \"MEAN_MOTION\": \"15.48915330\", \"ECCENTRICITY\": \"0.0007417\",\n  \"INCLINATION\": \"51.6432\", \"RA_OF_ASC_NODE\": \"351.4697\",\n  \"ARG_OF_PERICENTER\": \"130.5364\", \"MEAN_ANOMALY\": \"329.6482\",\n  \"BSTAR\": \"0.0001027\", \"MEAN_MOTION_DOT\": \"0.00016717\",\n  \"MEAN_MOTION_DDOT\": \"0\"\n}]\"\"\"\nwith tempfile.NamedTemporaryFile(\"w\", suffix=\".json\", delete=False) as fh:\n    fh.write(omm_json)\n    json_path = fh.name\n\n# The entire \"loader\": stdlib json -> list of dicts -> sgp4\nwith open(json_path) as fh:\n    omm_list = json.load(fh)\n\nepoch = sk.time(omm_list[0][\"EPOCH\"])\np_json, v_json = sk.sgp4(omm_list[0], epoch + sk.duration(minutes=30))\nprint(f\"Position from JSON file (m): {p_json}\")\nos.unlink(json_path)\n\n# --- XML file ------------------------------------------------------------\n# The same OMM in CCSDS XML. xmltodict parses it; navigate down to the\n# body/segment/data element and hand that dictionary to sgp4 — the nested\n# meanElements / tleParameters groups are understood directly.\nomm_xml = \"\"\"<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<ndm><omm id=\"CCSDS_OMM_VERS\" version=\"2.0\">\n  <body><segment>\n    <metadata>\n      <OBJECT_NAME>ISS (ZARYA)</OBJECT_NAME><OBJECT_ID>1998-067A</OBJECT_ID>\n      <CENTER_NAME>EARTH</CENTER_NAME><REF_FRAME>TEME</REF_FRAME>\n      <TIME_SYSTEM>UTC</TIME_SYSTEM>\n      <MEAN_ELEMENT_THEORY>SGP4</MEAN_ELEMENT_THEORY>\n    </metadata>\n    <data>\n      <meanElements>\n        <EPOCH>2021-10-02T14:10:59.999</EPOCH>\n        <MEAN_MOTION>15.48915330</MEAN_MOTION>\n        <ECCENTRICITY>0.0007417</ECCENTRICITY>\n        <INCLINATION>51.6432</INCLINATION>\n        <RA_OF_ASC_NODE>351.4697</RA_OF_ASC_NODE>\n        <ARG_OF_PERICENTER>130.5364</ARG_OF_PERICENTER>\n        <MEAN_ANOMALY>329.6482</MEAN_ANOMALY>\n      </meanElements>\n      <tleParameters>\n        <BSTAR>0.0001027</BSTAR>\n        <MEAN_MOTION_DOT>0.00016717</MEAN_MOTION_DOT>\n        <MEAN_MOTION_DDOT>0</MEAN_MOTION_DDOT>\n      </tleParameters>\n    </data>\n  </segment></body>\n</omm></ndm>\"\"\"\n\nparsed = xmltodict.parse(omm_xml)\n# Multi-satellite files have a list under [\"ndm\"][\"omm\"]; this one is a\n# single entry, so index straight down to the data element.\ndata = parsed[\"ndm\"][\"omm\"][\"body\"][\"segment\"][\"data\"]\np_xml, v_xml = sk.sgp4(data, epoch + sk.duration(minutes=30))\nprint(f\"Position from XML file  (m): {p_xml}\")\n\n# Same OMM, same epoch -> identical states from both file formats\nassert (p_json == p_xml).all() and (v_json == v_xml).all()\nprint(\"JSON and XML file routes agree.\")",
+   "source": "import json\nimport tempfile\nimport os\n\n# --- JSON file -----------------------------------------------------------\n# Write a small single-satellite OMM JSON file, standing in for a\n# Space-Track / CelesTrak bulk download saved to disk.\nomm_json = \"\"\"[{\n  \"OBJECT_NAME\": \"ISS (ZARYA)\", \"OBJECT_ID\": \"1998-067A\",\n  \"EPOCH\": \"2021-10-02T14:10:59.999\", \"TIME_SYSTEM\": \"UTC\",\n  \"MEAN_ELEMENT_THEORY\": \"SGP4\",\n  \"MEAN_MOTION\": \"15.48915330\", \"ECCENTRICITY\": \"0.0007417\",\n  \"INCLINATION\": \"51.6432\", \"RA_OF_ASC_NODE\": \"351.4697\",\n  \"ARG_OF_PERICENTER\": \"130.5364\", \"MEAN_ANOMALY\": \"329.6482\",\n  \"BSTAR\": \"0.0001027\", \"MEAN_MOTION_DOT\": \"0.00016717\",\n  \"MEAN_MOTION_DDOT\": \"0\"\n}]\"\"\"\nwith tempfile.NamedTemporaryFile(\"w\", suffix=\".json\", delete=False) as fh:\n    fh.write(omm_json)\n    json_path = fh.name\n\n# sk.omm_from_file detects JSON vs XML from the content and returns the\n# same list of dicts as sk.omm_from_url. (Plain json.load works too: sgp4\n# accepts the raw dict, quoted numbers and all.)\nomm_list = sk.omm_from_file(json_path)\nos.unlink(json_path)\n\nepoch = sk.time(omm_list[0][\"EPOCH\"])\np_json, v_json = sk.sgp4(omm_list[0], epoch + sk.duration(minutes=30))\nprint(f\"Position from JSON file (m): {p_json}\")\n\n# --- XML text ------------------------------------------------------------\n# The same OMM in CCSDS XML. sk.omm_from_text parses it directly; no XML\n# library or tree-walking needed.\nomm_xml = \"\"\"<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<ndm><omm id=\"CCSDS_OMM_VERS\" version=\"2.0\">\n  <body><segment>\n    <metadata>\n      <OBJECT_NAME>ISS (ZARYA)</OBJECT_NAME><OBJECT_ID>1998-067A</OBJECT_ID>\n      <CENTER_NAME>EARTH</CENTER_NAME><REF_FRAME>TEME</REF_FRAME>\n      <TIME_SYSTEM>UTC</TIME_SYSTEM>\n      <MEAN_ELEMENT_THEORY>SGP4</MEAN_ELEMENT_THEORY>\n    </metadata>\n    <data>\n      <meanElements>\n        <EPOCH>2021-10-02T14:10:59.999</EPOCH>\n        <MEAN_MOTION>15.48915330</MEAN_MOTION>\n        <ECCENTRICITY>0.0007417</ECCENTRICITY>\n        <INCLINATION>51.6432</INCLINATION>\n        <RA_OF_ASC_NODE>351.4697</RA_OF_ASC_NODE>\n        <ARG_OF_PERICENTER>130.5364</ARG_OF_PERICENTER>\n        <MEAN_ANOMALY>329.6482</MEAN_ANOMALY>\n      </meanElements>\n      <tleParameters>\n        <EPHEMERIS_TYPE>0</EPHEMERIS_TYPE>\n        <CLASSIFICATION_TYPE>U</CLASSIFICATION_TYPE>\n        <NORAD_CAT_ID>25544</NORAD_CAT_ID>\n        <ELEMENT_SET_NO>900</ELEMENT_SET_NO>\n        <REV_AT_EPOCH>29935</REV_AT_EPOCH>\n        <BSTAR>0.0001027</BSTAR>\n        <MEAN_MOTION_DOT>0.00016717</MEAN_MOTION_DOT>\n        <MEAN_MOTION_DDOT>0</MEAN_MOTION_DDOT>\n      </tleParameters>\n    </data>\n  </segment></body>\n</omm></ndm>\"\"\"\n\nomm_from_xml = sk.omm_from_text(omm_xml)[0]\np_xml, v_xml = sk.sgp4(omm_from_xml, epoch + sk.duration(minutes=30))\nprint(f\"Position from XML text  (m): {p_xml}\")\n\n# Same OMM, same epoch -> identical states from both formats\nassert (p_json == p_xml).all() and (v_json == v_xml).all()\nprint(\"JSON and XML routes agree.\")\n\n# --- TLE <-> OMM -----------------------------------------------------------\n# An OMM dict converts to a TLE object and back; the TLE lines are the\n# classic 69-character form of the same element set.\ntle = sk.TLE.from_omm(omm_from_xml)\nprint(\"\\n\".join(tle.to_2line()))\nprint(json.dumps(tle.to_omm(), indent=1)[:200], \"...\")\n",
    "metadata": {},
    "execution_count": null,
    "outputs": []

pyproject.toml

@@ -38,7 +38,7 @@ classifiers = [
     "Programming Language :: Python :: 3.14",
 ]
 [project.optional-dependencies]
-test = ["pytest", "xmltodict", "mypy", "scipy"]
+test = ["pytest", "mypy", "scipy"]
 # Offline data bundle (JPL ephemeris, full-degree gravity files, IERS tables);
 # not required — the core tables are compiled in and the ephemeris is
 # downloaded on first use.

python/Cargo.toml

@@ -19,6 +19,7 @@ numpy = "0.29"
 numeris = { version = "0.5.18", features = ["serde", "ode"] }
 anyhow = "1"
 serde = { version = "1.0", features = ["derive"] }
+serde_json = "1.0"
 serde-pickle = "1.2.0"
 process_path = "0.1.4"
 

python/satkit/__init__.py

@@ -3,6 +3,7 @@
 __version__ = version("satkit")
 
 from .satkit import *  # type: ignore
+from ._types import OMMDict
 
 # The core data (IERS nutation tables, gravity models to degree 70) is
 # compiled into the extension, so satkit works with no data directory at all.

python/satkit/__init__.pyi

@@ -1,4 +1,5 @@
 from .satkit import *
+from ._types import OMMDict
 
 from . import jplephem
 from . import frametransform
@@ -50,6 +51,9 @@ __all__ = [
     "propresult",
     "propstats",
     "omm_from_url",
+    "omm_from_file",
+    "omm_from_text",
+    "OMMDict",
     "tlefitstatus",
     "__version__",
 ]