From 683a17e83316e115a5d6ad02d7cb9ed8e931b78a Mon Sep 17 00:00:00 2001 From: Ryan Ketzner Date: Fri, 22 May 2026 12:59:29 -0700 Subject: [PATCH 1/7] Intermediate commit (not working) for file transfter. --- examples/propagator_validation.py | 225 ++++++++++++++++++++++++++++++ 1 file changed, 225 insertions(+) create mode 100644 examples/propagator_validation.py diff --git a/examples/propagator_validation.py b/examples/propagator_validation.py new file mode 100644 index 0000000..a052290 --- /dev/null +++ b/examples/propagator_validation.py @@ -0,0 +1,225 @@ +import numpy as np +import os +import matplotlib.pyplot as plt + +from eosimutils.time import AbsoluteDateArray +from eosimutils.trajectory import StateSeries, PositionSeries +from eosimutils.base import ReferenceFrame, SurfaceType +from eosimutils.state import CartesianState +from eosimutils.framegraph import FrameGraph + + +from orbitpy.specular import get_specular_trajectory, get_topk_trajectories + +import orbitpy.orekitpropagator # triggers decorator registration +from orbitpy.propagator import PropagatorFactory +from orbitpy.orbits import TwoLineElementSet, SpaceTrackAPI, OrbitalMeanElementsMessage + +def get_first_idx(arr): + """Get index of first non-naan row in numpy array""" + + mask = ~np.isnan(arr).all(axis=1) + first_idx = np.argmax(mask) + return first_idx + +def convert_datetime(np_dt): + """Convert numpy datetime object to AbsoluteDateArray + + Args: + np_dt (np.ndarray): Numpy datetime64 array. + + Returns: + AbsoluteDateArray: Orbitpy AbsoluteDateArray object with the times from the datetime object + """ + + arr = np.atleast_1d(np_dt) + + if not np.issubdtype(arr.dtype, np.datetime64): + raise TypeError("np_dt must be a numpy.datetime64 scalar or array") + + # Convert to millisecond precision ISO strings without timezone + arr_ms = arr.astype("datetime64[ms]") + iso = np.datetime_as_string(arr_ms, unit="ms", timezone="naive") + iso_list = [str(iso)] if np.isscalar(iso) else iso.astype(str).tolist() + + return AbsoluteDateArray.from_dict( + { + "time_format": "GREGORIAN_DATE", + "calendar_date": iso_list, + "time_scale": "UTC", + } + ) + +# ------------- +# READ IN DATA +# ------------- + +KM_TO_M = 1000.0 +M_TO_KM = 1.0 / 1000.0 + +sat_number = "03" +ddm_num = 1 +folder = os.path.join( + os.path.dirname(os.path.abspath(__file__)), "cygnss_data", "" +) +ext = ".npy" + +cyg_file = folder + "cygnss_spacecraft_trajectory.csv" +cyg_pos = data = np.loadtxt( + cyg_file, + delimiter=",", + skiprows=1, + usecols=(1, 2, 3) +)*M_TO_KM +cyg_vel = data = np.loadtxt( + cyg_file, + delimiter=",", + skiprows=1, + usecols=(4, 5, 6) +)*M_TO_KM + +ddm_timestamp_utc = np.loadtxt( + cyg_file, + delimiter=",", + skiprows=1, + usecols=(0,), + dtype="datetime64[ns]" +) + +id = "44" +norad_id = 26407 +gps_file = folder + "prn_" + id + "_trajectory.csv" +gps_pos = np.genfromtxt( + gps_file, + delimiter=",", + skip_header=1, + usecols=(1, 2, 3), + dtype=float, + filling_values=np.nan +)*M_TO_KM +gps_vel = np.genfromtxt( + gps_file, + delimiter=",", + skip_header=1, + usecols=(4, 5, 6), + dtype=float, + filling_values=np.nan +)*M_TO_KM + +# ----------------------------------- +# Process input into orbitpy objects +# ----------------------------------- + +# Create AbsoluteDateArray from ddm timestamps +datearray = convert_datetime(ddm_timestamp_utc) + +# Create Stateseries from CYGNSS positions/velocities +sc_stateseries = StateSeries( + time=datearray, data=[gps_pos, gps_vel], frame=ReferenceFrame.get("ITRF") +) + + +# Get first available state for receiver (should always start at zero) +first_idx = get_first_idx(sc_stateseries.data[0]) +first_pos = sc_stateseries.data[0][first_idx] +first_vel = sc_stateseries.data[1][first_idx] +first_state = np.hstack((first_pos, first_vel)) +start_time = datearray[first_idx] +stop_time = datearray[-1] +duration_days = (stop_time.to_spice_ephemeris_time() - start_time.to_spice_ephemeris_time())/86400.0 +first_state = CartesianState.from_array(first_state, frame=ReferenceFrame.get("ITRF"), time=datearray[first_idx]) + +registry = FrameGraph() +to_frame = ReferenceFrame.get("ICRF_EC") +first_state_icrf = registry.transform(first_state, to_frame, start_time) +sc_stateseries_icrf = registry.transform_series(sc_stateseries, to_frame) + +specs = { + "propagator_type": "OREKIT_PROPAGATOR", + "stepSize": 10, +} + +duration_days = duration_days # Propagate for 1 day +prop = PropagatorFactory.from_dict(specs) +prop.set_drag_coeff(0.0) + +sat_state_series = prop.execute( + t0=start_time, duration_days=duration_days, initial_state=first_state_icrf +) + +diff = sat_state_series - sc_stateseries_icrf + +plt.figure() +plt.plot(sat_state_series.time.ephemeris_time*(1/86400.0), diff.data[0][:, 0], label="Propagated X") +plt.plot(sat_state_series.time.ephemeris_time*(1/86400.0), diff.data[0][:, 1], label="Propagated Y") +plt.plot(sat_state_series.time.ephemeris_time*(1/86400.0), diff.data[0][:, 2], label="Propagated Z") +plt.xlabel("Ephemeris Time (s)") +plt.ylabel("Position (km)") +plt.title("Propagated Satellite Position Over Time") +plt.legend() + +# Plot error norm +plt.figure() +error_norm = np.linalg.norm(diff.data[0], axis=1) +plt.plot(sat_state_series.time.ephemeris_time*(1/86400.0), error_norm) +plt.xlabel("Ephemeris Time (s)") +plt.ylabel("Position Error Norm (km)") +plt.title("Norm of Position Error Between Propagated State and Reference State") + +## Try again using SGP4 instead. +file_dir = os.path.dirname(__file__) +api = SpaceTrackAPI(os.path.join(file_dir, "../spacetrack/credentials.json")) +# api = SpaceTrackAPI("path/to/credentials.json") +api.login() + +# Convert your AbsoluteDate to ISO 8601 string format +# If your AbsoluteDate object is 'my_date': +date_str = start_time.to_dict(time_format="Gregorian_Date")["calendar_date"] +omm_dict = api.get_closest_omm( + norad_id=49260, # NORAD catalog ID of your satellite + target_date_time=date_str, + within_days=1 # Optional: max days before target to search +) + +# Extract the TLE from the OMM +if omm_dict: + omm = OrbitalMeanElementsMessage.from_dict(omm_dict) + +# -------------------------- +# Plot orbital energy vs time +# -------------------------- + +MU_EARTH = 398600.4418 # km^3 / s^2 + +r = sc_stateseries_icrf.data[0] # km +v = sc_stateseries_icrf.data[1] # km/s +r_norm = np.linalg.norm(r, axis=1) +v_norm = np.linalg.norm(v, axis=1) +specific_energy = 0.5 * v_norm**2 - MU_EARTH / r_norm # km^2/s^2 +t = sc_stateseries_icrf.time.ephemeris_time +t_hours = (t - t[0]) / 3600.0 + +plt.figure() +plt.plot(t_hours, specific_energy) +plt.xlabel("Time since start (hours)") +plt.ylabel("Specific Orbital Energy (km$^2$/s$^2$)") +plt.title("Satellite Specific Orbital Energy Over Time") +plt.grid(True) +plt.legend() + +r = sat_state_series.data[0] # km +v = sat_state_series.data[1] # km/s +r_norm = np.linalg.norm(r, axis=1) +v_norm = np.linalg.norm(v, axis=1) +specific_energy = 0.5 * v_norm**2 - MU_EARTH / r_norm # km^2/s^2 +t = sat_state_series.time.ephemeris_time +t_hours = (t - t[0]) / 3600.0 + +plt.figure() +plt.plot(t_hours, specific_energy) +plt.xlabel("Time since start (hours)") +plt.ylabel("Specific Orbital Energy (km$^2$/s$^2$)") +plt.title("Satellite Specific Orbital Energy Over Time") +plt.grid(True) +plt.legend() +plt.show() \ No newline at end of file From f79de882a235b6056e6fd9ba89e0e68d447bf197 Mon Sep 17 00:00:00 2001 From: Ryan Ketzner Date: Tue, 2 Jun 2026 16:43:10 -0700 Subject: [PATCH 2/7] Commiting intermediate results. --- examples/glsdc.py | 202 +++++++++++++ examples/orbit_estimation.py | 168 +++++++++++ examples/propagator_validation.py | 102 ++++--- examples/solve_drag_coefficient.py | 435 +++++++++++++++++++++++++++ examples/specular_validation_prop.py | 199 ++++++++++++ orbitpy/orekitpropagator.py | 52 ++-- pyproject.toml | 4 +- 7 files changed, 1087 insertions(+), 75 deletions(-) create mode 100644 examples/glsdc.py create mode 100644 examples/orbit_estimation.py create mode 100644 examples/solve_drag_coefficient.py create mode 100644 examples/specular_validation_prop.py diff --git a/examples/glsdc.py b/examples/glsdc.py new file mode 100644 index 0000000..f376fde --- /dev/null +++ b/examples/glsdc.py @@ -0,0 +1,202 @@ +"""Generic gauss least-squares differential correction (GLSDC). + +It solves + + minimize (y - F(x))^T R^{-1} (y - F(x)) + +using differential corrections and a finite-difference Jacobian by default. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Callable, Optional + +import numpy as np + + +VectorFunction = Callable[[np.ndarray], np.ndarray] + + +@dataclass +class GLSDCResults: + x_hat: np.ndarray + y_hat: np.ndarray + residual: np.ndarray + jacobian: np.ndarray + normal_matrix: np.ndarray + covariance: np.ndarray + dx: np.ndarray + cost: float + iterations: int + converged: bool + x_history: list[np.ndarray] = field(default_factory=list) + cost_history: list[float] = field(default_factory=list) + + +class FiniteDifferenceJacobian: + """Central finite-difference Jacobian for vector-valued functions.""" + + def __init__(self, function: VectorFunction, eps: float = 1.0e-6): + if not np.isfinite(eps) or eps <= 0.0: + raise ValueError("eps must be positive and finite") + self.function = function + self.eps = float(eps) + + def __call__(self, x: np.ndarray) -> np.ndarray: + x = _as_column(x, "x") + y0 = _as_column(self.function(x), "F(x)") + + ny = y0.shape[0] + nx = x.shape[0] + jac = np.empty((ny, nx), dtype=float) + + for j in range(nx): + h = self.eps * max(1.0, abs(float(x[j, 0]))) + xp = x.copy() + xm = x.copy() + xp[j, 0] += h + xm[j, 0] -= h + + yp = _as_column(self.function(xp), "F(x + h)") + ym = _as_column(self.function(xm), "F(x - h)") + + if yp.shape != y0.shape or ym.shape != y0.shape: + raise ValueError("measurement model output dimension changed during finite difference") + + jac[:, j] = ((yp - ym) / (2.0 * h)).ravel() + + return jac + + +class GLSDC: + def __init__(self, max_iters: int = 50, tol: float = 1.0e-10, fd_eps: float = 1.0e-6): + if max_iters <= 0: + raise ValueError("max_iters must be positive") + if not np.isfinite(tol) or tol <= 0.0: + raise ValueError("tol must be positive and finite") + if not np.isfinite(fd_eps) or fd_eps <= 0.0: + raise ValueError("fd_eps must be positive and finite") + + self.max_iters = int(max_iters) + self.tol = float(tol) + self.fd_eps = float(fd_eps) + + def solve( + self, + F: VectorFunction, + x0: np.ndarray, + y: np.ndarray, + R: Optional[np.ndarray] = None, + jacobian: Optional[VectorFunction] = None, + verbose: bool = True, + ) -> GLSDCResults: + x = _as_column(x0, "x0") + y = _as_column(y, "y") + + nx = x.shape[0] + ny = y.shape[0] + + Rinv = _inverse_covariance(R, ny) + df = jacobian if jacobian is not None else FiniteDifferenceJacobian(F, eps=self.fd_eps) + + x_history = [x.copy()] + cost_history: list[float] = [] + dx = np.zeros_like(x) + converged = False + + for k in range(self.max_iters): + y_model = _as_column(F(x), "F(x)") + H = np.asarray(df(x), dtype=float) + _validate_dimensions(y_model, H, nx, ny) + + residual = y - y_model + cost = float((residual.T @ Rinv @ residual)[0, 0]) + normal_matrix = H.T @ Rinv @ H + rhs = H.T @ Rinv @ residual + + dx = _solve_normal_equations(normal_matrix, rhs) + x = x + dx + + x_history.append(x.copy()) + cost_history.append(cost) + + dx_norm = float(np.linalg.norm(dx)) + if verbose: + print("=" * 72) + print(f"GLSDC iteration {k + 1}") + print(f"x = {x.ravel()}") + print(f"dx = {dx.ravel()}") + print(f"|dx| = {dx_norm:.16e}") + print(f"cost = {cost:.16e}") + print(f"rms resid = {np.sqrt(np.mean(residual ** 2)):.16e}") + + if dx_norm < self.tol: + converged = True + break + + y_hat = _as_column(F(x), "F(x_hat)") + H = np.asarray(df(x), dtype=float) + _validate_dimensions(y_hat, H, nx, ny) + + residual = y - y_hat + normal_matrix = H.T @ Rinv @ H + covariance = np.linalg.pinv(normal_matrix) + final_cost = float((residual.T @ Rinv @ residual)[0, 0]) + + return GLSDCResults( + x_hat=x, + y_hat=y_hat, + residual=residual, + jacobian=H, + normal_matrix=normal_matrix, + covariance=covariance, + dx=dx, + cost=final_cost, + iterations=len(cost_history), + converged=converged, + x_history=x_history, + cost_history=cost_history, + ) + + +def _as_column(value: np.ndarray, name: str) -> np.ndarray: + arr = np.asarray(value, dtype=float) + if arr.ndim == 0: + arr = arr.reshape(1, 1) + elif arr.ndim == 1: + arr = arr.reshape(-1, 1) + elif arr.ndim == 2 and arr.shape[1] == 1: + pass + else: + raise ValueError(f"{name} must be convertible to a column vector") + return arr + + +def _inverse_covariance(R: Optional[np.ndarray], ny: int) -> np.ndarray: + if R is None: + return np.eye(ny) + + R = np.asarray(R, dtype=float) + if R.shape != (ny, ny): + raise ValueError(f"R must have shape ({ny}, {ny})") + + try: + chol = np.linalg.cholesky(R) + return np.linalg.solve(chol.T, np.linalg.solve(chol, np.eye(ny))) + except np.linalg.LinAlgError: + return np.linalg.pinv(R) + + +def _validate_dimensions(y_model: np.ndarray, H: np.ndarray, nx: int, ny: int) -> None: + if y_model.shape != (ny, 1): + raise ValueError(f"F(x) must have shape ({ny}, 1), got {y_model.shape}") + if H.shape != (ny, nx): + raise ValueError(f"Jacobian must have shape ({ny}, {nx}), got {H.shape}") + + +def _solve_normal_equations(normal_matrix: np.ndarray, rhs: np.ndarray) -> np.ndarray: + try: + return np.linalg.solve(normal_matrix, rhs) + except np.linalg.LinAlgError: + return np.linalg.pinv(normal_matrix) @ rhs diff --git a/examples/orbit_estimation.py b/examples/orbit_estimation.py new file mode 100644 index 0000000..85f01ba --- /dev/null +++ b/examples/orbit_estimation.py @@ -0,0 +1,168 @@ +import numpy as np +import os +import matplotlib.pyplot as plt + +from eosimutils.time import AbsoluteDate, AbsoluteDateArray +from eosimutils.trajectory import StateSeries, PositionSeries +from eosimutils.base import ReferenceFrame, SurfaceType +from eosimutils.state import CartesianState +from eosimutils.framegraph import FrameGraph + + +from orbitpy.specular import get_specular_trajectory, get_topk_trajectories + +import orbitpy.orekitpropagator # triggers decorator registration +from orbitpy.propagator import PropagatorFactory +from orbitpy.orbits import TwoLineElementSet, SpaceTrackAPI, OrbitalMeanElementsMessage + +def convert_datetime(np_dt): + """Convert numpy datetime object to AbsoluteDateArray + + Args: + np_dt (np.ndarray): Numpy datetime64 array. + + Returns: + AbsoluteDateArray: Orbitpy AbsoluteDateArray object with the times from the datetime object + """ + + arr = np.atleast_1d(np_dt) + + if not np.issubdtype(arr.dtype, np.datetime64): + raise TypeError("np_dt must be a numpy.datetime64 scalar or array") + + # Convert to millisecond precision ISO strings without timezone + arr_ms = arr.astype("datetime64[ms]") + iso = np.datetime_as_string(arr_ms, unit="ms", timezone="naive") + iso_list = [str(iso)] if np.isscalar(iso) else iso.astype(str).tolist() + + return AbsoluteDateArray.from_dict( + { + "time_format": "GREGORIAN_DATE", + "calendar_date": iso_list, + "time_scale": "UTC", + } + ) + +# ------------- +# READ IN DATA +# ------------- + +KM_TO_M = 1000.0 +M_TO_KM = 1.0 / 1000.0 + +sat_number = "03" +ddm_num = 1 +folder = os.path.join( + os.path.dirname(os.path.abspath(__file__)), "cygnss_data", "" +) +ext = ".npy" + +cyg_file = folder + "cygnss_spacecraft_trajectory.csv" +cyg_pos = data = np.loadtxt( + cyg_file, + delimiter=",", + skiprows=1, + usecols=(1, 2, 3) +)*M_TO_KM +cyg_vel = data = np.loadtxt( + cyg_file, + delimiter=",", + skiprows=1, + usecols=(4, 5, 6) +)*M_TO_KM + +ddm_timestamp_utc = np.loadtxt( + cyg_file, + delimiter=",", + skiprows=1, + usecols=(0,), + dtype="datetime64[ns]" +) + +norad_id = 41891 + +# ----------------------------------- +# Process input into orbitpy objects +# ----------------------------------- + +# Create AbsoluteDateArray from ddm timestamps +datearray = convert_datetime(ddm_timestamp_utc) + +sc_stateseries = StateSeries( + time=datearray, data=[cyg_pos, cyg_vel], frame=ReferenceFrame.get("ITRF") +) + + +# Transform reference data to inertial frame +registry = FrameGraph() +to_frame = ReferenceFrame.get("ICRF_EC") +sc_stateseries_icrf = registry.transform_series(sc_stateseries, to_frame) + +# Start and stop time of reference data +start_time = datearray[0] +stop_time = datearray[-1] + +# Setup propagator +specs = { + "propagator_type": "OREKIT_PROPAGATOR", + "stepSize": 10, +} +prop = PropagatorFactory.from_dict(specs) +prop.set_drag_coeff(0.0) + +# Use spacetrack to get initial tle +# Convert AbsoluteDate to ISO 8601 string format +date_str_start = start_time.to_dict(time_format="Gregorian_Date")["calendar_date"] +date_str_stop = stop_time.to_dict(time_format="Gregorian_Date")["calendar_date"] +file_dir = os.path.dirname(__file__) +api = SpaceTrackAPI(os.path.join(file_dir, "spacetrack/credentials.json")) +api.login() +omm_dict = api.get_closest_omm( + norad_id=norad_id, # NORAD catalog ID of your satellite + target_date_time=date_str_stop, + within_days=1 # Optional: max days before target to search +) + +# Extract the TLE and time from the OMM +if omm_dict: + tle_epoch = AbsoluteDate.from_dict({"time_scale" : omm_dict["TIME_SYSTEM"], "time_format" : "GREGORIAN_DATE", "calendar_date" : omm_dict["EPOCH"]}) + omm = OrbitalMeanElementsMessage.from_dict(omm_dict) + +# Create array of dates starting at TLE epoch and continuing until end of reference trajectory, +# with a 300 second step size (5 min) +datearray = AbsoluteDateArray.linspace(tle_epoch, stop_time, 300) + +omm = sc_stateseries_icrf.at(tle_epoch) +omm = np.hstack((omm[0][0], omm[1][0])) +omm = CartesianState.from_array(omm, frame=ReferenceFrame.get("ICRF_EC"), time=tle_epoch) +# Propagate starting from TLE to each of the time points in the date array +sat_state_series_sgp4 = prop.execute_2(times=datearray, initial_state=omm) + +# Resample reference trajectory onto these time points. +sc_stateseries_icrf = sc_stateseries_icrf.resample(datearray) + +# Take difference between propagated and reference states +diff_st = sat_state_series_sgp4 - sc_stateseries_icrf + +# -------------------------- +# Plot results +# -------------------------- + +# Plot individual error components +plt.figure() +plt.plot(sat_state_series_sgp4.time.ephemeris_time*(1/86400.0), diff_st.data[0][:, 0], label="Propagated X") +plt.plot(sat_state_series_sgp4.time.ephemeris_time*(1/86400.0), diff_st.data[0][:, 1], label="Propagated Y") +plt.plot(sat_state_series_sgp4.time.ephemeris_time*(1/86400.0), diff_st.data[0][:, 2], label="Propagated Z") +plt.xlabel("Ephemeris Time (days)") +plt.ylabel("Position (km)") +plt.title("Propagated Satellite Position Over Time") +plt.legend() + +# Plot error norm +plt.figure() +error_norm = np.linalg.norm(diff_st.data[0], axis=1) +plt.plot(sat_state_series_sgp4.time.ephemeris_time*(1/86400.0), error_norm) +plt.xlabel("Ephemeris Time (days)") +plt.ylabel("Position Error Norm (km)") +plt.title("Norm of Position Error Between Propagated State and Reference State") +plt.show() \ No newline at end of file diff --git a/examples/propagator_validation.py b/examples/propagator_validation.py index a052290..b7e18c3 100644 --- a/examples/propagator_validation.py +++ b/examples/propagator_validation.py @@ -87,7 +87,8 @@ def convert_datetime(np_dt): ) id = "44" -norad_id = 26407 +norad_id_gps = 26407 +norad_id = 41891 gps_file = folder + "prn_" + id + "_trajectory.csv" gps_pos = np.genfromtxt( gps_file, @@ -114,12 +115,15 @@ def convert_datetime(np_dt): datearray = convert_datetime(ddm_timestamp_utc) # Create Stateseries from CYGNSS positions/velocities +# sc_stateseries = StateSeries( +# time=datearray, data=[gps_pos, gps_vel], frame=ReferenceFrame.get("ITRF") +# ) sc_stateseries = StateSeries( - time=datearray, data=[gps_pos, gps_vel], frame=ReferenceFrame.get("ITRF") + time=datearray, data=[cyg_pos, cyg_vel], frame=ReferenceFrame.get("ITRF") ) -# Get first available state for receiver (should always start at zero) +# Get first available state for receiver first_idx = get_first_idx(sc_stateseries.data[0]) first_pos = sc_stateseries.data[0][first_idx] first_vel = sc_stateseries.data[1][first_idx] @@ -139,7 +143,7 @@ def convert_datetime(np_dt): "stepSize": 10, } -duration_days = duration_days # Propagate for 1 day +# duration_days = 1 # Propagate for 1 day prop = PropagatorFactory.from_dict(specs) prop.set_drag_coeff(0.0) @@ -149,34 +153,17 @@ def convert_datetime(np_dt): diff = sat_state_series - sc_stateseries_icrf -plt.figure() -plt.plot(sat_state_series.time.ephemeris_time*(1/86400.0), diff.data[0][:, 0], label="Propagated X") -plt.plot(sat_state_series.time.ephemeris_time*(1/86400.0), diff.data[0][:, 1], label="Propagated Y") -plt.plot(sat_state_series.time.ephemeris_time*(1/86400.0), diff.data[0][:, 2], label="Propagated Z") -plt.xlabel("Ephemeris Time (s)") -plt.ylabel("Position (km)") -plt.title("Propagated Satellite Position Over Time") -plt.legend() - -# Plot error norm -plt.figure() -error_norm = np.linalg.norm(diff.data[0], axis=1) -plt.plot(sat_state_series.time.ephemeris_time*(1/86400.0), error_norm) -plt.xlabel("Ephemeris Time (s)") -plt.ylabel("Position Error Norm (km)") -plt.title("Norm of Position Error Between Propagated State and Reference State") ## Try again using SGP4 instead. file_dir = os.path.dirname(__file__) -api = SpaceTrackAPI(os.path.join(file_dir, "../spacetrack/credentials.json")) -# api = SpaceTrackAPI("path/to/credentials.json") +api = SpaceTrackAPI(os.path.join(file_dir, "spacetrack/credentials.json")) api.login() # Convert your AbsoluteDate to ISO 8601 string format # If your AbsoluteDate object is 'my_date': date_str = start_time.to_dict(time_format="Gregorian_Date")["calendar_date"] omm_dict = api.get_closest_omm( - norad_id=49260, # NORAD catalog ID of your satellite + norad_id=norad_id, # NORAD catalog ID of your satellite target_date_time=date_str, within_days=1 # Optional: max days before target to search ) @@ -185,41 +172,56 @@ def convert_datetime(np_dt): if omm_dict: omm = OrbitalMeanElementsMessage.from_dict(omm_dict) +# Difference in seconds between spacetrack start time and CYGNSS data start time +time_diff = 8000 #abs(omm.epoch.to_spice_ephemeris_time() - start_time.to_spice_ephemeris_time()) +time_diff_days = time_diff / 86400.0 +sat_state_series_sgp4 = prop.execute( + t0=start_time, duration_days=duration_days+time_diff_days, initial_state=omm +) + +diff_st = sat_state_series_sgp4 - sc_stateseries_icrf + # -------------------------- -# Plot orbital energy vs time +# Plot results # -------------------------- -MU_EARTH = 398600.4418 # km^3 / s^2 - -r = sc_stateseries_icrf.data[0] # km -v = sc_stateseries_icrf.data[1] # km/s -r_norm = np.linalg.norm(r, axis=1) -v_norm = np.linalg.norm(v, axis=1) -specific_energy = 0.5 * v_norm**2 - MU_EARTH / r_norm # km^2/s^2 -t = sc_stateseries_icrf.time.ephemeris_time -t_hours = (t - t[0]) / 3600.0 +# Results for Orekit propagator initialized with state from CYGNSS data +# Plot individual error components plt.figure() -plt.plot(t_hours, specific_energy) -plt.xlabel("Time since start (hours)") -plt.ylabel("Specific Orbital Energy (km$^2$/s$^2$)") -plt.title("Satellite Specific Orbital Energy Over Time") -plt.grid(True) +plt.plot(sat_state_series.time.ephemeris_time*(1/86400.0), diff.data[0][:, 0], label="Propagated X") +plt.plot(sat_state_series.time.ephemeris_time*(1/86400.0), diff.data[0][:, 1], label="Propagated Y") +plt.plot(sat_state_series.time.ephemeris_time*(1/86400.0), diff.data[0][:, 2], label="Propagated Z") +plt.xlabel("Ephemeris Time (s)") +plt.ylabel("Position (km)") +plt.title("Propagated Satellite Position Over Time") plt.legend() -r = sat_state_series.data[0] # km -v = sat_state_series.data[1] # km/s -r_norm = np.linalg.norm(r, axis=1) -v_norm = np.linalg.norm(v, axis=1) -specific_energy = 0.5 * v_norm**2 - MU_EARTH / r_norm # km^2/s^2 -t = sat_state_series.time.ephemeris_time -t_hours = (t - t[0]) / 3600.0 +# Plot error norm +plt.figure() +error_norm = np.linalg.norm(diff.data[0], axis=1) +plt.plot(sat_state_series.time.ephemeris_time*(1/86400.0), error_norm) +plt.xlabel("Ephemeris Time (s)") +plt.ylabel("Position Error Norm (km)") +plt.title("Norm of Position Error Between Propagated State and Reference State") +# Results for SGP4 propagator initialized with TLE from SpaceTrack + +# Plot individual error components plt.figure() -plt.plot(t_hours, specific_energy) -plt.xlabel("Time since start (hours)") -plt.ylabel("Specific Orbital Energy (km$^2$/s$^2$)") -plt.title("Satellite Specific Orbital Energy Over Time") -plt.grid(True) +plt.plot(sat_state_series_sgp4.time.ephemeris_time*(1/86400.0), diff_st.data[0][:, 0], label="Propagated X") +plt.plot(sat_state_series_sgp4.time.ephemeris_time*(1/86400.0), diff_st.data[0][:, 1], label="Propagated Y") +plt.plot(sat_state_series_sgp4.time.ephemeris_time*(1/86400.0), diff_st.data[0][:, 2], label="Propagated Z") +plt.xlabel("Ephemeris Time (s)") +plt.ylabel("Position (km)") +plt.title("Propagated Satellite Position Over Time") plt.legend() + +# Plot error norm +plt.figure() +error_norm = np.linalg.norm(diff_st.data[0], axis=1) +plt.plot(sat_state_series_sgp4.time.ephemeris_time*(1/86400.0), error_norm) +plt.xlabel("Ephemeris Time (s)") +plt.ylabel("Position Error Norm (km)") +plt.title("Norm of Position Error Between Propagated State and Reference State") plt.show() \ No newline at end of file diff --git a/examples/solve_drag_coefficient.py b/examples/solve_drag_coefficient.py new file mode 100644 index 0000000..e03bba9 --- /dev/null +++ b/examples/solve_drag_coefficient.py @@ -0,0 +1,435 @@ +"""Estimate CYGNSS drag coefficient with GLSDC nonlinear least squares.""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Callable, Optional +import os + +import numpy as np +import matplotlib.pyplot as plt + +from eosimutils.time import AbsoluteDate, AbsoluteDateArray +from eosimutils.trajectory import StateSeries +from eosimutils.base import ReferenceFrame +from eosimutils.framegraph import FrameGraph + +import orbitpy.orekitpropagator # triggers decorator registration +from orbitpy.propagator import PropagatorFactory +from orbitpy.orbits import SpaceTrackAPI, OrbitalMeanElementsMessage + + +# ============================================================================= +# GLSDC IMPLEMENTATION +# ============================================================================= + +VectorFunction = Callable[[np.ndarray], np.ndarray] + + +@dataclass +class GLSDCResults: + x_hat: np.ndarray + y_hat: np.ndarray + residual: np.ndarray + jacobian: np.ndarray + normal_matrix: np.ndarray + covariance: np.ndarray + dx: np.ndarray + cost: float + iterations: int + converged: bool + x_history: list[np.ndarray] = field(default_factory=list) + cost_history: list[float] = field(default_factory=list) + + +class FiniteDifferenceJacobian: + def __init__(self, function: VectorFunction, eps: float = 1.0e-6): + self.function = function + self.eps = float(eps) + + def __call__(self, x: np.ndarray) -> np.ndarray: + x = _as_column(x, "x") + y0 = _as_column(self.function(x), "F(x)") + + ny = y0.shape[0] + nx = x.shape[0] + jac = np.empty((ny, nx), dtype=float) + + for j in range(nx): + h = self.eps * max(1.0, abs(float(x[j, 0]))) + + xp = x.copy() + xm = x.copy() + xp[j, 0] += h + xm[j, 0] -= h + + yp = _as_column(self.function(xp), "F(x + h)") + ym = _as_column(self.function(xm), "F(x - h)") + + jac[:, j] = ((yp - ym) / (2.0 * h)).ravel() + + return jac + + +class GLSDC: + def __init__(self, max_iters: int = 50, tol: float = 1.0e-10, fd_eps: float = 1.0e-6): + self.max_iters = int(max_iters) + self.tol = float(tol) + self.fd_eps = float(fd_eps) + + def solve( + self, + F: VectorFunction, + x0: np.ndarray, + y: np.ndarray, + R: Optional[np.ndarray] = None, + jacobian: Optional[VectorFunction] = None, + verbose: bool = True, + ) -> GLSDCResults: + x = _as_column(x0, "x0") + y = _as_column(y, "y") + + nx = x.shape[0] + ny = y.shape[0] + + Rinv = _inverse_covariance(R, ny) + df = jacobian if jacobian is not None else FiniteDifferenceJacobian(F, eps=self.fd_eps) + + x_history = [x.copy()] + cost_history = [] + dx = np.zeros_like(x) + converged = False + + for k in range(self.max_iters): + y_model = _as_column(F(x), "F(x)") + H = np.asarray(df(x), dtype=float) + _validate_dimensions(y_model, H, nx, ny) + + residual = y - y_model + cost = float((residual.T @ Rinv @ residual)[0, 0]) + + normal_matrix = H.T @ Rinv @ H + rhs = H.T @ Rinv @ residual + + dx = _solve_normal_equations(normal_matrix, rhs) + x = x + dx + + x_history.append(x.copy()) + cost_history.append(cost) + + dx_norm = float(np.linalg.norm(dx)) + + if verbose: + print("=" * 72) + print(f"GLSDC iteration {k + 1}") + print(f"x = {x.ravel()}") + print(f"dx = {dx.ravel()}") + print(f"|dx| = {dx_norm:.16e}") + print(f"cost = {cost:.16e}") + print(f"rms resid = {np.sqrt(np.mean(residual ** 2)):.16e}") + + if dx_norm < self.tol: + converged = True + break + + y_hat = _as_column(F(x), "F(x_hat)") + H = np.asarray(df(x), dtype=float) + _validate_dimensions(y_hat, H, nx, ny) + + residual = y - y_hat + normal_matrix = H.T @ Rinv @ H + covariance = np.linalg.pinv(normal_matrix) + final_cost = float((residual.T @ Rinv @ residual)[0, 0]) + + return GLSDCResults( + x_hat=x, + y_hat=y_hat, + residual=residual, + jacobian=H, + normal_matrix=normal_matrix, + covariance=covariance, + dx=dx, + cost=final_cost, + iterations=len(cost_history), + converged=converged, + x_history=x_history, + cost_history=cost_history, + ) + + +def _as_column(value: np.ndarray, name: str) -> np.ndarray: + arr = np.asarray(value, dtype=float) + + if arr.ndim == 0: + return arr.reshape(1, 1) + + if arr.ndim == 1: + return arr.reshape(-1, 1) + + if arr.ndim == 2 and arr.shape[1] == 1: + return arr + + raise ValueError(f"{name} must be convertible to a column vector") + + +def _inverse_covariance(R: Optional[np.ndarray], ny: int) -> np.ndarray: + if R is None: + return np.eye(ny) + + R = np.asarray(R, dtype=float) + + try: + chol = np.linalg.cholesky(R) + return np.linalg.solve(chol.T, np.linalg.solve(chol, np.eye(ny))) + except np.linalg.LinAlgError: + return np.linalg.pinv(R) + + +def _validate_dimensions(y_model: np.ndarray, H: np.ndarray, nx: int, ny: int) -> None: + if y_model.shape != (ny, 1): + raise ValueError(f"F(x) must have shape ({ny}, 1), got {y_model.shape}") + if H.shape != (ny, nx): + raise ValueError(f"Jacobian must have shape ({ny}, {nx}), got {H.shape}") + + +def _solve_normal_equations(normal_matrix: np.ndarray, rhs: np.ndarray) -> np.ndarray: + try: + return np.linalg.solve(normal_matrix, rhs) + except np.linalg.LinAlgError: + return np.linalg.pinv(normal_matrix) @ rhs + + +# ============================================================================= +# ORBIT / DATA UTILITIES +# ============================================================================= + +def convert_datetime(np_dt): + arr = np.atleast_1d(np_dt) + + if not np.issubdtype(arr.dtype, np.datetime64): + raise TypeError("np_dt must be a numpy.datetime64 scalar or array") + + arr_ms = arr.astype("datetime64[ms]") + iso = np.datetime_as_string(arr_ms, unit="ms", timezone="naive") + iso_list = [str(iso)] if np.isscalar(iso) else iso.astype(str).tolist() + + return AbsoluteDateArray.from_dict( + { + "time_format": "GREGORIAN_DATE", + "calendar_date": iso_list, + "time_scale": "UTC", + } + ) + + +# ============================================================================= +# MAIN SCRIPT +# ============================================================================= + +def main(): + KM_TO_M = 1000.0 + M_TO_KM = 1.0 / 1000.0 + + norad_id = 41891 + + file_dir = os.path.dirname(os.path.abspath(__file__)) + + cyg_file = os.path.join( + file_dir, + "cygnss_data", + "cygnss_spacecraft_trajectory.csv", + ) + + credentials_file = os.path.join( + file_dir, + "spacetrack", + "credentials.json", + ) + + # ------------------------------------------------------------------------- + # Read CYGNSS truth trajectory + # ------------------------------------------------------------------------- + + cyg_pos = np.loadtxt( + cyg_file, + delimiter=",", + skiprows=1, + usecols=(1, 2, 3), + ) * M_TO_KM + + cyg_vel = np.loadtxt( + cyg_file, + delimiter=",", + skiprows=1, + usecols=(4, 5, 6), + ) * M_TO_KM + + ddm_timestamp_utc = np.loadtxt( + cyg_file, + delimiter=",", + skiprows=1, + usecols=(0,), + dtype="datetime64[ns]", + ) + + original_datearray = convert_datetime(ddm_timestamp_utc) + + sc_stateseries = StateSeries( + time=original_datearray, + data=[cyg_pos, cyg_vel], + frame=ReferenceFrame.get("ITRF"), + ) + + # Transform measured/reference data to inertial frame + registry = FrameGraph() + to_frame = ReferenceFrame.get("ICRF_EC") + sc_stateseries_icrf_full = registry.transform_series(sc_stateseries, to_frame) + + start_time = original_datearray[0] + stop_time = original_datearray[-1] + + # ------------------------------------------------------------------------- + # Build propagator + # ------------------------------------------------------------------------- + + specs = { + "propagator_type": "OREKIT_PROPAGATOR", + "stepSize": 10, + } + + prop = PropagatorFactory.from_dict(specs) + + # ------------------------------------------------------------------------- + # Get closest OMM from Space-Track + # ------------------------------------------------------------------------- + + date_str_stop = stop_time.to_dict(time_format="Gregorian_Date")["calendar_date"] + + api = SpaceTrackAPI(credentials_file) + api.login() + + omm_dict = api.get_closest_omm( + norad_id=norad_id, + target_date_time=date_str_stop, + within_days=1, + ) + + if not omm_dict: + raise RuntimeError("Could not retrieve OMM from Space-Track.") + + tle_epoch = AbsoluteDate.from_dict( + { + "time_scale": omm_dict["TIME_SYSTEM"], + "time_format": "GREGORIAN_DATE", + "calendar_date": omm_dict["EPOCH"], + } + ) + + omm = OrbitalMeanElementsMessage.from_dict(omm_dict) + + # ------------------------------------------------------------------------- + # Define measurement epochs + # ------------------------------------------------------------------------- + + datearray = AbsoluteDateArray.linspace(tle_epoch, stop_time, 300) + + # Resample reference trajectory onto propagation epochs + sc_stateseries_icrf = sc_stateseries_icrf_full.resample(datearray) + + # Reference positions: shape (M, 3) + y_reference_positions = sc_stateseries_icrf.data[0] + + # GLSDC requires column vector: shape (3M, 1) + y = y_reference_positions.reshape(-1, 1) + + # ------------------------------------------------------------------------- + # Define measurement model F(x) + # ------------------------------------------------------------------------- + + def F(x: np.ndarray) -> np.ndarray: + drag_coeff = float(np.asarray(x).reshape(-1)[0]) + + prop.set_drag_coeff(drag_coeff) + + propagated_state_series = prop.execute_2( + times=datearray, + initial_state=omm, + ) + + propagated_positions = propagated_state_series.data[0] + + return propagated_positions.reshape(-1, 1) + + # ------------------------------------------------------------------------- + # Solve for drag coefficient + # ------------------------------------------------------------------------- + + x0 = np.array([[300.2]]) + + solver = GLSDC( + max_iters=3, + tol=1.0e-8, + fd_eps=1.0e-2, + ) + + results = solver.solve( + F=F, + x0=x0, + y=y, + R=None, + verbose=True, + ) + + cd_hat = float(results.x_hat[0, 0]) + cd_sigma = float(np.sqrt(results.covariance[0, 0])) + + print() + print("=" * 80) + print("FINAL RESULTS") + print("=" * 80) + print(f"Converged: {results.converged}") + print(f"Iterations: {results.iterations}") + print(f"Estimated drag coefficient: {cd_hat:.16e}") + print(f"1-sigma formal uncertainty: {cd_sigma:.16e}") + print(f"Final cost: {results.cost:.16e}") + print(f"Final RMS position resid: {np.sqrt(np.mean(results.residual ** 2)):.16e} km") + + # ------------------------------------------------------------------------- + # Plot final residuals + # ------------------------------------------------------------------------- + + final_prop_positions = results.y_hat.reshape((-1, 3)) + diff_pos = final_prop_positions - y_reference_positions + error_norm = np.linalg.norm(diff_pos, axis=1) + + time_days = datearray.ephemeris_time * (1.0 / 86400.0) + + plt.figure() + plt.plot(time_days, diff_pos[:, 0], label="X") + plt.plot(time_days, diff_pos[:, 1], label="Y") + plt.plot(time_days, diff_pos[:, 2], label="Z") + plt.xlabel("Ephemeris Time (days)") + plt.ylabel("Position Residual (km)") + plt.title(f"Final Position Residuals, Cd = {cd_hat:.6f}") + plt.legend() + plt.grid(True) + + plt.figure() + plt.plot(time_days, error_norm) + plt.xlabel("Ephemeris Time (days)") + plt.ylabel("Position Error Norm (km)") + plt.title("Final Position Error Norm") + plt.grid(True) + + plt.figure() + plt.plot([float(x[0, 0]) for x in results.x_history], marker="o") + plt.xlabel("Iteration") + plt.ylabel("Drag Coefficient") + plt.title("Drag Coefficient Convergence") + plt.grid(True) + + plt.show() + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/examples/specular_validation_prop.py b/examples/specular_validation_prop.py new file mode 100644 index 0000000..8fc2b67 --- /dev/null +++ b/examples/specular_validation_prop.py @@ -0,0 +1,199 @@ +""" +Example script demonstrating specular point computation and switching algorithm, which +can select the top (or top-k) specular point trajectories based on the range-corrected +gain factor. The trajectory of rank "k" corresponds to the specular point trajectory with +the k-th highest RCG factor at each time point, using data from any number of GPS trasmitters. + +The simulation uses saved data for CYGNSS position and GPS transmitter positions, and computes the +specular point generated by each GPS/CYGNSS pair. + +Prints the percentage of time points where all four of the top 4 GPS satellites selected by the switching +algorithm match the top 4 GPS satellites from the reference data. + +Then, at each time point, selects the specular point with the lowest error norm vs the reference +specular point trajectory, and plots the error norm over time. + +Next, compares the trajectory of the specular point on the fourth channel from the CYGNSS data to +the trajectory of the specular point generated by the corresponding GPS satellite, chooosing +from among the top-four trajectories. The error norm vs the reference specular point trajectory +is plotted over time. +""" + +import numpy as np +import os +import matplotlib.pyplot as plt + +from eosimutils.time import AbsoluteDateArray +from eosimutils.trajectory import StateSeries, PositionSeries +from eosimutils.base import ReferenceFrame, SurfaceType +from eosimutils.state import CartesianState + +from orbitpy.specular import get_specular_trajectory, get_topk_trajectories +from orbitpy.propagator import PropagatorFactory +from orbitpy.orbits import TwoLineElementSet + +def get_first_idx(arr): + """Get index of first non-naan row in numpy array""" + + mask = ~np.isnan(arr).all(axis=1) + first_idx = np.argmax(mask) + return first_idx + +def convert_datetime(np_dt): + """Convert numpy datetime object to AbsoluteDateArray + + Args: + np_dt (np.ndarray): Numpy datetime64 array. + + Returns: + AbsoluteDateArray: Orbitpy AbsoluteDateArray object with the times from the datetime object + """ + + arr = np.atleast_1d(np_dt) + + if not np.issubdtype(arr.dtype, np.datetime64): + raise TypeError("np_dt must be a numpy.datetime64 scalar or array") + + # Convert to millisecond precision ISO strings without timezone + arr_ms = arr.astype("datetime64[ms]") + iso = np.datetime_as_string(arr_ms, unit="ms", timezone="naive") + iso_list = [str(iso)] if np.isscalar(iso) else iso.astype(str).tolist() + + return AbsoluteDateArray.from_dict( + { + "time_format": "GREGORIAN_DATE", + "calendar_date": iso_list, + "time_scale": "UTC", + } + ) + +# ------------- +# READ IN DATA +# ------------- + +KM_TO_M = 1000.0 +M_TO_KM = 1.0 / 1000.0 + +sat_number = "03" +ddm_num = 1 +folder = os.path.join( + os.path.dirname(os.path.abspath(__file__)), "cygnss_data", "" +) +ext = ".npy" + +cyg_file = folder + "cygnss_spacecraft_trajectory.csv" +cyg_pos = data = np.loadtxt( + cyg_file, + delimiter=",", + skiprows=1, + usecols=(1, 2, 3) +)*M_TO_KM +cyg_vel = data = np.loadtxt( + cyg_file, + delimiter=",", + skiprows=1, + usecols=(4, 5, 6) +)*M_TO_KM + +specular_file = folder + "specular_point_positions_ddm3.csv" +sp_pos = np.loadtxt( + specular_file, + delimiter=",", + skiprows=1, + usecols=(1, 2, 3) +)*M_TO_KM + +ddm_timestamp_utc = np.loadtxt( + cyg_file, + delimiter=",", + skiprows=1, + usecols=(0,), + dtype="datetime64[ns]" +) + +gps_ids = ["43", "44"] +output_list = [] +tx_list = [] +cyg_list = [] +tx_starters = [] +cyg_starters = [] + +for id in gps_ids: + print(f"Processing GPS satellite PRN {id}...") + + gps_file = folder + "prn_" + id + "_trajectory.csv" + gps_pos = np.genfromtxt( + gps_file, + delimiter=",", + skip_header=1, + usecols=(1, 2, 3), + dtype=float, + filling_values=np.nan + )*M_TO_KM + gps_vel = np.genfromtxt( + gps_file, + delimiter=",", + skip_header=1, + usecols=(4, 5, 6), + dtype=float, + filling_values=np.nan + )*M_TO_KM + + + # ----------------------------------- + # Process input into orbitpy objects + # ----------------------------------- + + # Create AbsoluteDateArray from ddm timestamps + datearray = convert_datetime(ddm_timestamp_utc) + + # Create Stateseries from CYGNSS positions/velocities + sc_stateseries = StateSeries( + time=datearray, data=[cyg_pos, cyg_vel], frame=ReferenceFrame.get("ITRF") + ) + + cyg_list.append(sc_stateseries) + + # Create Stateseries from GPS positions/velocities + tx_stateseries = StateSeries( + time=datearray, data=[gps_pos, gps_vel], frame=ReferenceFrame.get("ITRF") + ) + + # Get first available state for transmitter + tx_list.append(tx_stateseries) + first_idx = get_first_idx(tx_stateseries.data[0]) + first_pos = tx_stateseries.data[0][get_first_idx(first_idx)] + first_vel = tx_stateseries.data[1][get_first_idx(first_idx)] + first_state = np.hstack((first_pos, first_vel)) + first_state = CartesianState.from_array(first_state, frame=ReferenceFrame.get("ITRF"), time=datearray[first_idx]) + tx_starters.append(first_state) + + # Get first available state for receiver (should always start at zero) + first_idx = get_first_idx(sc_stateseries.data[0]) + first_pos = sc_stateseries.data[0][get_first_idx(first_idx)] + first_vel = sc_stateseries.data[1][get_first_idx(first_idx)] + first_state = np.hstack((first_pos, first_vel)) + first_state = CartesianState.from_array(first_state, frame=ReferenceFrame.get("ITRF"), time=datearray[first_idx]) + cyg_starters.append(first_state) + + # Create PositionSeries from specular point positions + sp_posseries_ref = PositionSeries( + time=datearray, data=sp_pos, frame=ReferenceFrame.get("ITRF") + ) + + # Compute specular points using ellipsoidal Earth model + sp_posseries_ellipsoidal, rcg_factor_ellipsoidal = get_specular_trajectory( + transmitter_states_itrf=tx_stateseries, + receiver_states_itrf=sc_stateseries, + times=datearray, + surface=SurfaceType.WGS84, + ) + + output_list.append((sp_posseries_ellipsoidal, rcg_factor_ellipsoidal)) + + +plt.figure() +plt.plot(datearray.ephemeris_time, tx_stateseries.data[0][:, 0], label="Reference Specular Point X") +plt.xlabel("Ephemeris Time (s)") +plt.ylabel("va") +plt.show() \ No newline at end of file diff --git a/orbitpy/orekitpropagator.py b/orbitpy/orekitpropagator.py index db676a3..3761dd4 100644 --- a/orbitpy/orekitpropagator.py +++ b/orbitpy/orekitpropagator.py @@ -315,23 +315,26 @@ def to_dict(self) -> Dict[str, Any]: "use_moon_third_body": self.use_moon_third_body } - def execute( + def execute_2( self, - t0: AbsoluteDate, - duration_days: float, + times: AbsoluteDateArray, initial_state: Union[CartesianState, TwoLineElementSet, OrbitalMeanElementsMessage], ) -> StateSeries: - """Propagate an initial Cartesian state using the configured Orekit model. + """Propagate an initial Cartesian state at the requested times. Args: - t0: Start time for propagation. - duration_days: Duration of propagation in days. + times: AbsoluteDateArray defining the output epochs. initial_state: Initial state. Returns: - StateSeries: Trajectory sampled at `self.step_size`-second intervals. + StateSeries: Trajectory sampled at the requested times, containing Cartesian position (km) and velocity (km/s) in the ICRF_EC frame. """ + if len(times) == 0: + raise ValueError("times must contain at least one epoch") + + t0 = times[0] + # Convert start time to Orekit AbsoluteDate using astropy py_datetime = t0.to_astropy_time().to_datetime() orekit_start_date = datetime_to_absolutedate(py_datetime) @@ -392,26 +395,15 @@ def execute( self.propagator.resetInitialState(ic) self.propagator.setOrbitType(OrbitType.CARTESIAN) - # Setup time grid - duration_sec = float(duration_days) * 86400.0 - step_time = float(self.step_size) - num_steps = int(np.floor(duration_sec / step_time)) + 1 - positions: list[list[float]] = [] velocities: list[list[float]] = [] - # Build the time array for output - t0_et = float(t0.ephemeris_time) - - et_array = np.empty(num_steps, dtype=float) - et_array[0] = t0_et - # Propagate - for k in range(num_steps): - dt = float(k) * step_time - if k > 0: - et_array[k] = t0_et + dt + t0_et = float(t0.ephemeris_time) + et_array = times.ephemeris_time.astype(float, copy=True) + for k, et in enumerate(et_array): + dt = float(et - t0_et) tt = orekit_start_date.shiftedBy(dt) st = self.propagator.propagate(tt) @@ -433,4 +425,18 @@ def execute( time=absolute_date_array, data=[positions_arr, velocities_arr], frame=reference_frame, - ) \ No newline at end of file + ) + + def execute( + self, + t0: AbsoluteDate, + duration_days: float, + initial_state: Union[CartesianState, TwoLineElementSet, OrbitalMeanElementsMessage], + ) -> StateSeries: + """Backward-compatible wrapper that builds the sample times from t0 and duration.""" + duration_sec = float(duration_days) * 86400.0 + step_time = float(self.step_size) + num_steps = int(np.floor(duration_sec / step_time)) + 1 + et_array = t0.ephemeris_time + np.arange(num_steps, dtype=float) * step_time + times = AbsoluteDateArray(et_array) + return self.execute_2(times=times, initial_state=initial_state) \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index a239fdf..8b167d4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,10 +36,10 @@ dev = [ ] [tool.scikit-build] -wheel.packages = ["src/orbitpy"] +wheel.packages = ["orbitpy"] [tool.scikit-build.cmake.define] CMAKE_BUILD_TYPE = "Release" [tool.black] -line-length = 80 \ No newline at end of file +line-length = 80 From 1ad3e07369af8539371e44de4f5e1a3ec8369e1b Mon Sep 17 00:00:00 2001 From: Ryan Ketzner Date: Fri, 19 Jun 2026 09:20:41 -0700 Subject: [PATCH 3/7] Added orekit to dependencies. It was supposed to be there before. Modified: environment.yml: Bug fix: added orekit to dependencies. pyproject.toml: Bug fix: added orekit to dependencies. --- environment.yml | 1 + pyproject.toml | 3 ++- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index cc3f643..2faec8d 100644 --- a/environment.yml +++ b/environment.yml @@ -4,6 +4,7 @@ channels: dependencies: - python=3.13 - pip + - orekit # portable build tools - cmake diff --git a/pyproject.toml b/pyproject.toml index 8b167d4..3be4df6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,7 +22,8 @@ dependencies = [ "astropy", "skyfield", "spiceypy", - "requests" + "requests", + "orekit" ] [project.optional-dependencies] From 3d75379a3755bd3ddca3079848a5f7a175c913b8 Mon Sep 17 00:00:00 2001 From: Ryan Ketzner Date: Mon, 13 Jul 2026 18:02:56 -0700 Subject: [PATCH 4/7] Updated drag coefficient solving code my moving glsdc into its own module. Added example which also solves x0. Deleted: examples/glsdc.py: Deleted to create new example which uses module for glsdc code rather than inline code. Modified: examples/solve_drag_coefficient.py New: orbitpy/glsdc.py: Code for glsdc. Also includes code for finite differencing jacobians. examples/solve_drag_coefficient_x0.py: Solves drag coefficient and initial state x0. Very slow due to finite difference. --- .../cygnss_spacecraft_trajectory_combined.csv | 518308 +++++++++++++++ examples/glsdc.py | 202 - examples/solve_drag_coefficient.py | 244 +- examples/solve_drag_coefficient_x0.py | 331 + orbitpy/glsdc.py | 412 + 5 files changed, 519091 insertions(+), 406 deletions(-) create mode 100644 examples/cygnss_data/cygnss_spacecraft_trajectory_combined.csv delete mode 100644 examples/glsdc.py create mode 100644 examples/solve_drag_coefficient_x0.py create mode 100644 orbitpy/glsdc.py diff --git a/examples/cygnss_data/cygnss_spacecraft_trajectory_combined.csv b/examples/cygnss_data/cygnss_spacecraft_trajectory_combined.csv new file mode 100644 index 0000000..7c97c90 --- /dev/null +++ b/examples/cygnss_data/cygnss_spacecraft_trajectory_combined.csv @@ -0,0 +1,518308 @@ +time,sc_pos_x,sc_pos_y,sc_pos_z,sc_vel_x,sc_vel_y,sc_vel_z +2024-01-01 00:00:00.499261608,2794521.0,4999511.0,-3806226.0,-6534.0,2806.0,-1122.0 +2024-01-01 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08:25:46.499261819,3240016.0,5820167.0,1679838.0,-5693.0,2016.0,3946.0 +2024-01-01 08:25:46.999261772,3237169.0,5821177.0,1681812.0,-5695.0,2013.0,3945.0 +2024-01-01 08:25:47.499261594,3234320.0,5822180.0,1683783.0,-5696.0,2010.0,3944.0 +2024-01-01 08:25:47.999261533,3231472.0,5823183.0,1685755.0,-5698.0,2007.0,3943.0 +2024-01-01 08:25:48.499261542,3228621.0,5824185.0,1687727.0,-5700.0,2004.0,3942.0 +2024-01-01 08:25:48.999261623,3225770.0,5825188.0,1689698.0,-5702.0,2000.0,3941.0 +2024-01-01 08:25:49.499261694,3222917.0,5826187.0,1691669.0,-5704.0,1997.0,3940.0 +2024-01-01 08:25:49.999261767,3220065.0,5827186.0,1693639.0,-5706.0,1994.0,3939.0 +2024-01-01 08:25:50.499261775,3217211.0,5828182.0,1695608.0,-5707.0,1991.0,3938.0 +2024-01-01 08:25:50.999261721,3214358.0,5829178.0,1697577.0,-5709.0,1988.0,3937.0 +2024-01-01 08:25:51.499261543,3211502.0,5830170.0,1699546.0,-5711.0,1985.0,3936.0 +2024-01-01 08:25:51.999261486,3208646.0,5831162.0,1701514.0,-5713.0,1982.0,3935.0 +2024-01-01 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10:06:33.499261708,3240206.0,5251216.0,3010106.0,-6259.0,2239.0,2806.0 +2024-01-01 10:06:33.999261771,3237075.0,5252336.0,3011511.0,-6261.0,2237.0,2804.0 +2024-01-01 10:06:34.499261775,3233944.0,5253452.0,3012912.0,-6263.0,2234.0,2802.0 +2024-01-01 10:06:34.999261715,3230813.0,5254568.0,3014313.0,-6265.0,2231.0,2800.0 +2024-01-01 10:06:35.499261532,3227680.0,5255685.0,3015713.0,-6266.0,2228.0,2798.0 +2024-01-01 10:06:35.999261477,3224548.0,5256801.0,3017113.0,-6268.0,2225.0,2796.0 +2024-01-01 10:06:36.499261543,3221412.0,5257912.0,3018510.0,-6270.0,2223.0,2795.0 +2024-01-01 10:06:36.999261598,3218277.0,5259024.0,3019907.0,-6272.0,2220.0,2793.0 +2024-01-01 10:06:37.499261602,3215139.0,5260132.0,3021303.0,-6274.0,2217.0,2791.0 +2024-01-01 10:06:37.999261674,3212001.0,5261239.0,3022698.0,-6275.0,2215.0,2789.0 +2024-01-01 10:06:38.499261740,3208862.0,5262346.0,3024092.0,-6277.0,2212.0,2787.0 +2024-01-01 10:06:38.999261809,3205722.0,5263452.0,3025486.0,-6279.0,2209.0,2785.0 +2024-01-01 10:06:39.499261813,3202583.0,5264557.0,3026877.0,-6281.0,2206.0,2783.0 +2024-01-01 10:06:39.999261758,3199443.0,5265661.0,3028269.0,-6283.0,2203.0,2782.0 +2024-01-01 10:06:40.499261575,3196301.0,5266762.0,3029660.0,-6284.0,2201.0,2780.0 +2024-01-01 10:06:40.999261511,3193159.0,5267863.0,3031051.0,-6286.0,2198.0,2778.0 +2024-01-01 10:06:41.499261577,3190014.0,5268959.0,3032438.0,-6288.0,2195.0,2776.0 +2024-01-01 10:06:41.999261640,3186869.0,5270055.0,3033825.0,-6290.0,2192.0,2774.0 +2024-01-01 10:06:42.499261706,3183724.0,5271152.0,3035213.0,-6292.0,2190.0,2772.0 +2024-01-01 10:06:42.999261717,3180579.0,5272249.0,3036601.0,-6293.0,2187.0,2770.0 +2024-01-01 10:06:43.499261783,3177431.0,5273340.0,3037984.0,-6295.0,2184.0,2768.0 +2024-01-01 10:06:43.999261710,3174283.0,5274430.0,3039367.0,-6297.0,2181.0,2767.0 +2024-01-01 10:06:44.499261527,3171133.0,5275520.0,3040751.0,-6299.0,2178.0,2765.0 +2024-01-01 10:06:44.999261481,3167982.0,5276610.0,3042135.0,-6301.0,2176.0,2763.0 +2024-01-01 10:06:45.499261547,3164831.0,5277697.0,3043515.0,-6302.0,2173.0,2761.0 +2024-01-01 10:06:45.999261547,3161680.0,5278783.0,3044895.0,-6304.0,2170.0,2759.0 +2024-01-01 10:06:46.499261613,3158526.0,5279866.0,3046274.0,-6306.0,2167.0,2757.0 +2024-01-01 10:06:46.999261682,3155371.0,5280949.0,3047652.0,-6308.0,2165.0,2755.0 +2024-01-01 10:06:47.499261748,3152216.0,5282030.0,3049028.0,-6310.0,2162.0,2753.0 +2024-01-01 10:06:47.999261758,3149060.0,5283110.0,3050404.0,-6311.0,2159.0,2752.0 +2024-01-01 10:06:48.499261824,3145904.0,5284189.0,3051780.0,-6313.0,2156.0,2750.0 +2024-01-01 10:06:48.999261771,3142747.0,5285267.0,3053156.0,-6315.0,2153.0,2748.0 +2024-01-01 10:06:49.499261588,3139590.0,5286343.0,3054529.0,-6317.0,2151.0,2746.0 +2024-01-01 10:06:49.999261529,3136434.0,5287419.0,3055902.0,-6318.0,2148.0,2744.0 +2024-01-01 10:06:50.499261595,3133272.0,5288492.0,3057274.0,-6320.0,2145.0,2742.0 +2024-01-01 10:06:50.999261592,3130111.0,5289565.0,3058645.0,-6322.0,2142.0,2740.0 +2024-01-01 10:06:51.499261658,3126949.0,5290636.0,3060015.0,-6324.0,2139.0,2738.0 +2024-01-01 10:06:51.999261726,3123787.0,5291707.0,3061385.0,-6325.0,2137.0,2736.0 +2024-01-01 10:06:52.499261792,3120623.0,5292776.0,3062752.0,-6327.0,2134.0,2735.0 +2024-01-01 10:06:52.999261726,3117459.0,5293844.0,3064120.0,-6329.0,2131.0,2733.0 +2024-01-01 10:06:53.499261543,3114294.0,5294907.0,3065485.0,-6331.0,2128.0,2731.0 +2024-01-01 10:06:53.999261419,3111128.0,5295970.0,3066851.0,-6333.0,2126.0,2729.0 +2024-01-01 10:06:54.499261486,3107962.0,5297032.0,3068215.0,-6334.0,2123.0,2727.0 +2024-01-01 10:06:54.999261557,3104796.0,5298094.0,3069578.0,-6336.0,2120.0,2725.0 +2024-01-01 10:06:55.499261623,3101626.0,5299153.0,3070941.0,-6338.0,2117.0,2723.0 +2024-01-01 10:06:55.999261621,3098455.0,5300212.0,3072303.0,-6339.0,2114.0,2721.0 +2024-01-01 10:06:56.499261687,3095284.0,5301268.0,3073662.0,-6341.0,2112.0,2719.0 +2024-01-01 10:06:56.999261753,3092113.0,5302323.0,3075022.0,-6343.0,2109.0,2717.0 +2024-01-01 10:06:57.499261819,3088941.0,5303377.0,3076379.0,-6345.0,2106.0,2716.0 +2024-01-01 10:06:57.999261768,3085770.0,5304430.0,3077737.0,-6346.0,2103.0,2714.0 +2024-01-01 10:06:58.499261584,3082595.0,5305480.0,3079093.0,-6348.0,2101.0,2712.0 +2024-01-01 10:06:58.999261461,3079419.0,5306529.0,3080450.0,-6350.0,2098.0,2710.0 +2024-01-01 10:06:59.499261527,3076243.0,5307577.0,3081803.0,-6352.0,2095.0,2708.0 +2024-01-01 10:06:59.999261598,3073066.0,5308624.0,3083156.0,-6353.0,2092.0,2706.0 +2024-01-01 10:07:00.499261664,3069890.0,5309671.0,3084509.0,-6355.0,2089.0,2704.0 +2024-01-01 10:07:00.999261673,3066713.0,5310717.0,3085863.0,-6357.0,2086.0,2702.0 +2024-01-01 10:07:01.499261739,3063534.0,5311759.0,3087214.0,-6359.0,2084.0,2700.0 +2024-01-01 10:07:01.999261793,3060354.0,5312800.0,3088565.0,-6360.0,2081.0,2698.0 +2024-01-01 10:07:02.499261859,3057172.0,5313839.0,3089914.0,-6362.0,2078.0,2697.0 +2024-01-01 10:07:02.999261803,3053990.0,5314879.0,3091263.0,-6364.0,2075.0,2695.0 +2024-01-01 10:07:03.499261620,3050808.0,5315916.0,3092608.0,-6365.0,2073.0,2693.0 +2024-01-01 10:07:03.999261559,3047625.0,5316953.0,3093954.0,-6367.0,2070.0,2691.0 +2024-01-01 10:07:04.499261562,3044440.0,5317986.0,3095299.0,-6369.0,2067.0,2689.0 +2024-01-01 10:07:04.999261628,3041254.0,5319020.0,3096644.0,-6371.0,2064.0,2687.0 +2024-01-01 10:07:05.499261694,3038067.0,5320050.0,3097986.0,-6372.0,2061.0,2685.0 +2024-01-01 10:07:05.999261761,3034880.0,5321080.0,3099329.0,-6374.0,2059.0,2683.0 +2024-01-01 10:07:06.499261764,3031693.0,5322109.0,3100671.0,-6376.0,2056.0,2681.0 +2024-01-01 10:07:06.999261715,3028505.0,5323138.0,3102012.0,-6377.0,2053.0,2679.0 +2024-01-01 10:07:07.499261531,3025315.0,5324163.0,3103351.0,-6379.0,2050.0,2677.0 +2024-01-01 10:07:07.999261463,3022125.0,5325188.0,3104690.0,-6381.0,2047.0,2676.0 +2024-01-01 10:07:08.499261529,3018934.0,5326210.0,3106027.0,-6383.0,2045.0,2674.0 +2024-01-01 10:07:08.999261601,3015744.0,5327232.0,3107365.0,-6384.0,2042.0,2672.0 +2024-01-01 10:07:09.499261605,3012550.0,5328252.0,3108699.0,-6386.0,2039.0,2670.0 +2024-01-01 10:07:09.999261668,3009357.0,5329272.0,3110034.0,-6388.0,2036.0,2668.0 +2024-01-01 10:07:10.499261734,3006162.0,5330289.0,3111366.0,-6389.0,2033.0,2666.0 +2024-01-01 10:07:10.999261799,3002967.0,5331305.0,3112698.0,-6391.0,2031.0,2664.0 +2024-01-01 10:07:11.499261803,2999770.0,5332320.0,3114032.0,-6393.0,2028.0,2662.0 +2024-01-01 10:07:11.999261749,2996573.0,5333335.0,3115365.0,-6394.0,2025.0,2660.0 +2024-01-01 10:07:12.499261565,2993375.0,5334346.0,3116693.0,-6396.0,2022.0,2658.0 +2024-01-01 10:07:12.999261500,2990177.0,5335357.0,3118021.0,-6398.0,2019.0,2656.0 +2024-01-01 10:07:13.499261566,2986977.0,5336366.0,3119349.0,-6399.0,2017.0,2654.0 +2024-01-01 10:07:13.999261637,2983777.0,5337376.0,3120677.0,-6401.0,2014.0,2652.0 +2024-01-01 10:07:14.499261641,2980576.0,5338382.0,3122002.0,-6403.0,2011.0,2650.0 +2024-01-01 10:07:14.999261702,2977375.0,5339388.0,3123328.0,-6405.0,2008.0,2649.0 +2024-01-01 10:07:15.499261768,2974170.0,5340390.0,3124651.0,-6406.0,2005.0,2647.0 +2024-01-01 10:07:15.999261706,2970965.0,5341392.0,3125975.0,-6408.0,2002.0,2645.0 +2024-01-01 10:07:16.499261522,2967761.0,5342393.0,3127297.0,-6410.0,2000.0,2643.0 +2024-01-01 10:07:16.999261469,2964557.0,5343394.0,3128620.0,-6411.0,1997.0,2641.0 +2024-01-01 10:07:17.499261473,2961351.0,5344390.0,3129938.0,-6413.0,1994.0,2639.0 +2024-01-01 10:07:17.999261532,2958144.0,5345386.0,3131257.0,-6414.0,1991.0,2637.0 +2024-01-01 10:07:18.499261598,2954936.0,5346381.0,3132575.0,-6416.0,1988.0,2635.0 +2024-01-01 10:07:18.999261669,2951728.0,5347376.0,3133893.0,-6418.0,1986.0,2633.0 +2024-01-01 10:07:19.499261673,2948517.0,5348367.0,3135209.0,-6420.0,1983.0,2631.0 +2024-01-01 10:07:19.999261737,2945306.0,5349358.0,3136525.0,-6421.0,1980.0,2629.0 +2024-01-01 10:07:20.499261803,2942096.0,5350347.0,3137838.0,-6423.0,1977.0,2627.0 +2024-01-01 10:07:20.999261748,2938885.0,5351336.0,3139151.0,-6425.0,1974.0,2625.0 +2024-01-01 10:07:21.499261564,2935671.0,5352322.0,3140462.0,-6426.0,1972.0,2623.0 +2024-01-01 10:07:21.999261505,2932457.0,5353308.0,3141774.0,-6428.0,1969.0,2621.0 +2024-01-01 10:07:22.499261509,2929241.0,5354292.0,3143086.0,-6430.0,1966.0,2619.0 +2024-01-01 10:07:22.999261578,2926026.0,5355277.0,3144397.0,-6431.0,1963.0,2618.0 +2024-01-01 10:07:23.499261643,2922810.0,5356258.0,3145705.0,-6433.0,1960.0,2616.0 +2024-01-01 10:07:23.999261702,2919594.0,5357239.0,3147013.0,-6434.0,1958.0,2614.0 +2024-01-01 10:07:24.499261768,2916375.0,5358215.0,3148319.0,-6436.0,1955.0,2612.0 +2024-01-01 10:07:24.999261642,2913157.0,5359192.0,3149624.0,-6438.0,1952.0,2610.0 +2024-01-01 10:07:25.499261458,2909937.0,5360166.0,3150928.0,-6439.0,1949.0,2608.0 +2024-01-01 10:07:25.999261408,2906717.0,5361140.0,3152233.0,-6441.0,1946.0,2606.0 +2024-01-01 10:07:26.499261474,2903496.0,5362111.0,3153535.0,-6443.0,1943.0,2604.0 +2024-01-01 10:07:26.999261541,2900274.0,5363083.0,3154837.0,-6444.0,1941.0,2602.0 +2024-01-01 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11:49:12.499261590,2127342.0,5236981.0,3889043.0,-6813.0,2300.0,618.0 +2024-01-01 11:49:12.999261523,2123935.0,5238130.0,3889352.0,-6814.0,2297.0,616.0 +2024-01-01 11:49:13.499261524,2120527.0,5239277.0,3889658.0,-6815.0,2294.0,614.0 +2024-01-01 11:49:13.999261590,2117120.0,5240424.0,3889965.0,-6816.0,2291.0,611.0 +2024-01-01 11:49:14.499261652,2113712.0,5241570.0,3890270.0,-6817.0,2289.0,609.0 +2024-01-01 11:49:14.999261719,2110303.0,5242716.0,3890575.0,-6818.0,2286.0,606.0 +2024-01-01 11:49:15.499261720,2106892.0,5243857.0,3890877.0,-6820.0,2283.0,604.0 +2024-01-01 11:49:15.999261776,2103480.0,5244997.0,3891179.0,-6821.0,2280.0,602.0 +2024-01-01 11:49:16.499261839,2100069.0,5246137.0,3891479.0,-6822.0,2278.0,599.0 +2024-01-01 11:49:16.999261784,2096658.0,5247277.0,3891779.0,-6823.0,2275.0,597.0 +2024-01-01 11:49:17.499261597,2093245.0,5248412.0,3892076.0,-6824.0,2272.0,594.0 +2024-01-01 11:49:17.999261532,2089832.0,5249547.0,3892373.0,-6825.0,2270.0,592.0 +2024-01-01 11:49:18.499261595,2086420.0,5250681.0,3892668.0,-6826.0,2267.0,590.0 +2024-01-01 11:49:18.999261603,2083007.0,5251815.0,3892964.0,-6827.0,2264.0,587.0 +2024-01-01 11:49:19.499261665,2079594.0,5252946.0,3893257.0,-6829.0,2261.0,585.0 +2024-01-01 11:49:19.999261727,2076181.0,5254078.0,3893549.0,-6830.0,2259.0,582.0 +2024-01-01 11:49:20.499261790,2072764.0,5255206.0,3893839.0,-6831.0,2256.0,580.0 +2024-01-01 11:49:20.999261659,2069347.0,5256334.0,3894129.0,-6832.0,2253.0,578.0 +2024-01-01 11:49:21.499261472,2065930.0,5257459.0,3894417.0,-6833.0,2250.0,575.0 +2024-01-01 11:49:21.999261412,2062513.0,5258583.0,3894706.0,-6834.0,2248.0,573.0 +2024-01-01 11:49:22.499261475,2059095.0,5259707.0,3894990.0,-6835.0,2245.0,570.0 +2024-01-01 11:49:22.999261543,2055677.0,5260831.0,3895275.0,-6836.0,2242.0,568.0 +2024-01-01 11:49:23.499261606,2052258.0,5261952.0,3895558.0,-6837.0,2240.0,565.0 +2024-01-01 11:49:23.999261603,2048840.0,5263072.0,3895841.0,-6839.0,2237.0,563.0 +2024-01-01 11:49:24.499261665,2045420.0,5264188.0,3896122.0,-6840.0,2234.0,561.0 +2024-01-01 11:49:24.999261727,2041999.0,5265304.0,3896403.0,-6841.0,2231.0,558.0 +2024-01-01 11:49:25.499261790,2038578.0,5266419.0,3896681.0,-6842.0,2229.0,556.0 +2024-01-01 11:49:25.999261731,2035157.0,5267534.0,3896959.0,-6843.0,2226.0,553.0 +2024-01-01 11:49:26.499261545,2031735.0,5268646.0,3897235.0,-6844.0,2223.0,551.0 +2024-01-01 11:49:26.999261419,2028313.0,5269758.0,3897511.0,-6845.0,2220.0,549.0 +2024-01-01 11:49:27.499261482,2024889.0,5270868.0,3897784.0,-6846.0,2218.0,546.0 +2024-01-01 11:49:27.999261542,2021466.0,5271977.0,3898058.0,-6847.0,2215.0,544.0 +2024-01-01 11:49:28.499261605,2018042.0,5273084.0,3898328.0,-6848.0,2212.0,541.0 +2024-01-01 11:49:28.999261606,2014618.0,5274191.0,3898598.0,-6849.0,2210.0,539.0 +2024-01-01 11:49:29.499261669,2011193.0,5275294.0,3898867.0,-6850.0,2207.0,537.0 +2024-01-01 11:49:29.999261729,2007768.0,5276396.0,3899136.0,-6852.0,2204.0,534.0 +2024-01-01 11:49:30.499261792,2004341.0,5277497.0,3899402.0,-6853.0,2201.0,532.0 +2024-01-01 11:49:30.999261733,2000913.0,5278598.0,3899669.0,-6854.0,2199.0,529.0 +2024-01-01 11:49:31.499261547,1997485.0,5279695.0,3899932.0,-6855.0,2196.0,527.0 +2024-01-01 11:49:31.999261423,1994057.0,5280792.0,3900195.0,-6856.0,2193.0,525.0 +2024-01-01 11:49:32.499261486,1990630.0,5281889.0,3900458.0,-6857.0,2190.0,522.0 +2024-01-01 11:49:32.999261555,1987203.0,5282985.0,3900720.0,-6858.0,2188.0,520.0 +2024-01-01 11:49:33.499261618,1983773.0,5284078.0,3900978.0,-6859.0,2185.0,517.0 +2024-01-01 11:49:33.999261612,1980343.0,5285170.0,3901236.0,-6860.0,2182.0,515.0 +2024-01-01 11:49:34.499261674,1976912.0,5286261.0,3901493.0,-6861.0,2179.0,512.0 +2024-01-01 11:49:34.999261736,1973481.0,5287352.0,3901749.0,-6862.0,2177.0,510.0 +2024-01-01 11:49:35.499261799,1970049.0,5288439.0,3902003.0,-6863.0,2174.0,508.0 +2024-01-01 11:49:35.999261734,1966618.0,5289525.0,3902257.0,-6864.0,2171.0,505.0 +2024-01-01 11:49:36.499261547,1963184.0,5290610.0,3902509.0,-6865.0,2168.0,503.0 +2024-01-01 11:49:36.999261424,1959750.0,5291694.0,3902760.0,-6866.0,2166.0,500.0 +2024-01-01 11:49:37.499261487,1956318.0,5292775.0,3903009.0,-6867.0,2163.0,498.0 +2024-01-01 11:49:37.999261554,1952885.0,5293856.0,3903259.0,-6868.0,2160.0,496.0 +2024-01-01 11:49:38.499261617,1949450.0,5294936.0,3903506.0,-6869.0,2157.0,493.0 +2024-01-01 11:49:38.999261614,1946014.0,5296016.0,3903753.0,-6870.0,2155.0,491.0 +2024-01-01 11:49:39.499261676,1942578.0,5297092.0,3903997.0,-6871.0,2152.0,488.0 +2024-01-01 11:49:39.999261737,1939142.0,5298167.0,3904241.0,-6873.0,2149.0,486.0 +2024-01-01 11:49:40.499261800,1935705.0,5299240.0,3904483.0,-6874.0,2146.0,483.0 +2024-01-01 11:49:40.999261741,1932267.0,5300313.0,3904724.0,-6875.0,2144.0,481.0 +2024-01-01 11:49:41.499261554,1928829.0,5301383.0,3904964.0,-6876.0,2141.0,479.0 +2024-01-01 11:49:41.999261497,1925390.0,5302453.0,3905204.0,-6877.0,2138.0,476.0 +2024-01-01 11:49:42.499261497,1921951.0,5303522.0,3905440.0,-6878.0,2135.0,474.0 +2024-01-01 11:49:42.999261563,1918511.0,5304590.0,3905676.0,-6879.0,2133.0,471.0 +2024-01-01 11:49:43.499261626,1915072.0,5305655.0,3905911.0,-6880.0,2130.0,469.0 +2024-01-01 11:49:43.999261687,1911632.0,5306719.0,3906146.0,-6881.0,2127.0,467.0 +2024-01-01 11:49:44.499261687,1908190.0,5307781.0,3906378.0,-6882.0,2124.0,464.0 +2024-01-01 11:49:44.999261751,1904749.0,5308843.0,3906610.0,-6883.0,2122.0,462.0 +2024-01-01 11:49:45.499261814,1901308.0,5309903.0,3906841.0,-6884.0,2119.0,459.0 +2024-01-01 11:49:45.999261760,1897867.0,5310963.0,3907072.0,-6885.0,2116.0,457.0 +2024-01-01 11:49:46.499261574,1894424.0,5312021.0,3907300.0,-6886.0,2113.0,454.0 +2024-01-01 11:49:46.999261504,1890981.0,5313079.0,3907528.0,-6887.0,2111.0,452.0 +2024-01-01 11:49:47.499261504,1887537.0,5314134.0,3907753.0,-6888.0,2108.0,450.0 +2024-01-01 11:49:47.999261561,1884092.0,5315189.0,3907978.0,-6889.0,2105.0,447.0 +2024-01-01 11:49:48.499261624,1880647.0,5316239.0,3908201.0,-6890.0,2102.0,445.0 +2024-01-01 11:49:48.999261683,1877202.0,5317290.0,3908424.0,-6891.0,2100.0,442.0 +2024-01-01 11:49:49.499261683,1873756.0,5318340.0,3908644.0,-6892.0,2097.0,440.0 +2024-01-01 11:49:49.999261749,1870310.0,5319389.0,3908864.0,-6893.0,2094.0,438.0 +2024-01-01 11:49:50.499261812,1866862.0,5320434.0,3909081.0,-6894.0,2091.0,435.0 +2024-01-01 11:49:50.999261745,1863414.0,5321478.0,3909298.0,-6895.0,2088.0,433.0 +2024-01-01 11:49:51.499261558,1859967.0,5322522.0,3909514.0,-6896.0,2086.0,430.0 +2024-01-01 11:49:51.999261505,1856519.0,5323566.0,3909731.0,-6897.0,2083.0,428.0 +2024-01-01 11:49:52.499261505,1853070.0,5324606.0,3909943.0,-6898.0,2080.0,425.0 +2024-01-01 11:49:52.999261564,1849620.0,5325647.0,3910156.0,-6899.0,2077.0,423.0 +2024-01-01 11:49:53.499261627,1846170.0,5326685.0,3910366.0,-6900.0,2075.0,421.0 +2024-01-01 11:49:53.999261691,1842719.0,5327723.0,3910575.0,-6901.0,2072.0,418.0 +2024-01-01 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09:17:13.499261534,2982108.0,5300964.0,3187279.0,-6423.0,2064.0,2553.0 +2024-01-03 09:17:13.999261475,2978895.0,5301995.0,3188556.0,-6425.0,2061.0,2551.0 +2024-01-03 09:17:14.499261545,2975683.0,5303024.0,3189831.0,-6426.0,2058.0,2549.0 +2024-01-03 09:17:14.999261623,2972470.0,5304054.0,3191106.0,-6428.0,2056.0,2547.0 +2024-01-03 09:17:15.499261694,2969255.0,5305081.0,3192378.0,-6430.0,2053.0,2545.0 +2024-01-03 09:17:15.999261701,2966039.0,5306107.0,3193651.0,-6431.0,2050.0,2543.0 +2024-01-03 09:17:16.499261771,2962823.0,5307130.0,3194922.0,-6433.0,2047.0,2541.0 +2024-01-03 09:17:16.999261716,2959606.0,5308153.0,3196193.0,-6435.0,2044.0,2539.0 +2024-01-03 09:17:17.499261536,2956387.0,5309175.0,3197462.0,-6436.0,2042.0,2537.0 +2024-01-03 09:17:17.999261486,2953169.0,5310196.0,3198731.0,-6438.0,2039.0,2535.0 +2024-01-03 09:17:18.499261557,2949949.0,5311215.0,3199998.0,-6440.0,2036.0,2533.0 +2024-01-03 09:17:18.999261565,2946729.0,5312233.0,3201266.0,-6441.0,2033.0,2532.0 +2024-01-03 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10:59:58.999261776,1791952.0,5338395.0,3921948.0,-6895.0,2108.0,269.0 +2024-01-03 10:59:59.499261592,1788503.0,5339447.0,3922081.0,-6896.0,2105.0,267.0 +2024-01-03 10:59:59.999261530,1785054.0,5340499.0,3922215.0,-6897.0,2102.0,264.0 +2024-01-03 11:00:00.499261533,1781605.0,5341549.0,3922346.0,-6898.0,2099.0,262.0 +2024-01-03 11:00:00.999261605,1778156.0,5342599.0,3922478.0,-6899.0,2096.0,259.0 +2024-01-03 11:00:01.499261670,1774707.0,5343646.0,3922607.0,-6900.0,2094.0,257.0 +2024-01-03 11:00:01.999261735,1771257.0,5344693.0,3922735.0,-6901.0,2091.0,255.0 +2024-01-03 11:00:02.499261737,1767804.0,5345737.0,3922861.0,-6902.0,2088.0,252.0 +2024-01-03 11:00:02.999261802,1764352.0,5346781.0,3922987.0,-6903.0,2085.0,250.0 +2024-01-03 11:00:03.499261867,1760899.0,5347822.0,3923111.0,-6904.0,2083.0,248.0 +2024-01-03 11:00:03.999261812,1757447.0,5348863.0,3923235.0,-6905.0,2080.0,245.0 +2024-01-03 11:00:04.499261628,1753994.0,5349902.0,3923356.0,-6906.0,2077.0,243.0 +2024-01-03 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23:59:58.499261648,5938475.0,-3434097.0,-258157.0,2745.0,5059.0,-4362.0 +2024-01-03 23:59:58.999261725,5939849.0,-3431568.0,-260338.0,2741.0,5061.0,-4362.0 +2024-01-03 23:59:59.499261729,5941218.0,-3429036.0,-262518.0,2738.0,5063.0,-4362.0 +2024-01-03 23:59:59.999261783,5942586.0,-3426504.0,-264699.0,2735.0,5065.0,-4361.0 diff --git a/examples/glsdc.py b/examples/glsdc.py deleted file mode 100644 index f376fde..0000000 --- a/examples/glsdc.py +++ /dev/null @@ -1,202 +0,0 @@ -"""Generic gauss least-squares differential correction (GLSDC). - -It solves - - minimize (y - F(x))^T R^{-1} (y - F(x)) - -using differential corrections and a finite-difference Jacobian by default. -""" - -from __future__ import annotations - -from dataclasses import dataclass, field -from typing import Callable, Optional - -import numpy as np - - -VectorFunction = Callable[[np.ndarray], np.ndarray] - - -@dataclass -class GLSDCResults: - x_hat: np.ndarray - y_hat: np.ndarray - residual: np.ndarray - jacobian: np.ndarray - normal_matrix: np.ndarray - covariance: np.ndarray - dx: np.ndarray - cost: float - iterations: int - converged: bool - x_history: list[np.ndarray] = field(default_factory=list) - cost_history: list[float] = field(default_factory=list) - - -class FiniteDifferenceJacobian: - """Central finite-difference Jacobian for vector-valued functions.""" - - def __init__(self, function: VectorFunction, eps: float = 1.0e-6): - if not np.isfinite(eps) or eps <= 0.0: - raise ValueError("eps must be positive and finite") - self.function = function - self.eps = float(eps) - - def __call__(self, x: np.ndarray) -> np.ndarray: - x = _as_column(x, "x") - y0 = _as_column(self.function(x), "F(x)") - - ny = y0.shape[0] - nx = x.shape[0] - jac = np.empty((ny, nx), dtype=float) - - for j in range(nx): - h = self.eps * max(1.0, abs(float(x[j, 0]))) - xp = x.copy() - xm = x.copy() - xp[j, 0] += h - xm[j, 0] -= h - - yp = _as_column(self.function(xp), "F(x + h)") - ym = _as_column(self.function(xm), "F(x - h)") - - if yp.shape != y0.shape or ym.shape != y0.shape: - raise ValueError("measurement model output dimension changed during finite difference") - - jac[:, j] = ((yp - ym) / (2.0 * h)).ravel() - - return jac - - -class GLSDC: - def __init__(self, max_iters: int = 50, tol: float = 1.0e-10, fd_eps: float = 1.0e-6): - if max_iters <= 0: - raise ValueError("max_iters must be positive") - if not np.isfinite(tol) or tol <= 0.0: - raise ValueError("tol must be positive and finite") - if not np.isfinite(fd_eps) or fd_eps <= 0.0: - raise ValueError("fd_eps must be positive and finite") - - self.max_iters = int(max_iters) - self.tol = float(tol) - self.fd_eps = float(fd_eps) - - def solve( - self, - F: VectorFunction, - x0: np.ndarray, - y: np.ndarray, - R: Optional[np.ndarray] = None, - jacobian: Optional[VectorFunction] = None, - verbose: bool = True, - ) -> GLSDCResults: - x = _as_column(x0, "x0") - y = _as_column(y, "y") - - nx = x.shape[0] - ny = y.shape[0] - - Rinv = _inverse_covariance(R, ny) - df = jacobian if jacobian is not None else FiniteDifferenceJacobian(F, eps=self.fd_eps) - - x_history = [x.copy()] - cost_history: list[float] = [] - dx = np.zeros_like(x) - converged = False - - for k in range(self.max_iters): - y_model = _as_column(F(x), "F(x)") - H = np.asarray(df(x), dtype=float) - _validate_dimensions(y_model, H, nx, ny) - - residual = y - y_model - cost = float((residual.T @ Rinv @ residual)[0, 0]) - normal_matrix = H.T @ Rinv @ H - rhs = H.T @ Rinv @ residual - - dx = _solve_normal_equations(normal_matrix, rhs) - x = x + dx - - x_history.append(x.copy()) - cost_history.append(cost) - - dx_norm = float(np.linalg.norm(dx)) - if verbose: - print("=" * 72) - print(f"GLSDC iteration {k + 1}") - print(f"x = {x.ravel()}") - print(f"dx = {dx.ravel()}") - print(f"|dx| = {dx_norm:.16e}") - print(f"cost = {cost:.16e}") - print(f"rms resid = {np.sqrt(np.mean(residual ** 2)):.16e}") - - if dx_norm < self.tol: - converged = True - break - - y_hat = _as_column(F(x), "F(x_hat)") - H = np.asarray(df(x), dtype=float) - _validate_dimensions(y_hat, H, nx, ny) - - residual = y - y_hat - normal_matrix = H.T @ Rinv @ H - covariance = np.linalg.pinv(normal_matrix) - final_cost = float((residual.T @ Rinv @ residual)[0, 0]) - - return GLSDCResults( - x_hat=x, - y_hat=y_hat, - residual=residual, - jacobian=H, - normal_matrix=normal_matrix, - covariance=covariance, - dx=dx, - cost=final_cost, - iterations=len(cost_history), - converged=converged, - x_history=x_history, - cost_history=cost_history, - ) - - -def _as_column(value: np.ndarray, name: str) -> np.ndarray: - arr = np.asarray(value, dtype=float) - if arr.ndim == 0: - arr = arr.reshape(1, 1) - elif arr.ndim == 1: - arr = arr.reshape(-1, 1) - elif arr.ndim == 2 and arr.shape[1] == 1: - pass - else: - raise ValueError(f"{name} must be convertible to a column vector") - return arr - - -def _inverse_covariance(R: Optional[np.ndarray], ny: int) -> np.ndarray: - if R is None: - return np.eye(ny) - - R = np.asarray(R, dtype=float) - if R.shape != (ny, ny): - raise ValueError(f"R must have shape ({ny}, {ny})") - - try: - chol = np.linalg.cholesky(R) - return np.linalg.solve(chol.T, np.linalg.solve(chol, np.eye(ny))) - except np.linalg.LinAlgError: - return np.linalg.pinv(R) - - -def _validate_dimensions(y_model: np.ndarray, H: np.ndarray, nx: int, ny: int) -> None: - if y_model.shape != (ny, 1): - raise ValueError(f"F(x) must have shape ({ny}, 1), got {y_model.shape}") - if H.shape != (ny, nx): - raise ValueError(f"Jacobian must have shape ({ny}, {nx}), got {H.shape}") - - -def _solve_normal_equations(normal_matrix: np.ndarray, rhs: np.ndarray) -> np.ndarray: - try: - return np.linalg.solve(normal_matrix, rhs) - except np.linalg.LinAlgError: - return np.linalg.pinv(normal_matrix) @ rhs diff --git a/examples/solve_drag_coefficient.py b/examples/solve_drag_coefficient.py index e03bba9..5c854e5 100644 --- a/examples/solve_drag_coefficient.py +++ b/examples/solve_drag_coefficient.py @@ -1,4 +1,14 @@ -"""Estimate CYGNSS drag coefficient with GLSDC nonlinear least squares.""" +"""Estimate CYGNSS drag coefficient with GLSDC nonlinear least squares. + +Solves for the drag coefficient using an ~3 day measurement arc consisting +of CYGNSS reference position data, which is sampled every 300 seconds. +The initial state is initialized using a TLE from celestrak. + +After four iterations, this solves for a drag coefficient of -2.36. The negative +drag coefficient probably implies that the CYGNSS reference velocity data +is not very accurate, and this error is being absorbed into the drag coefficient. + +""" from __future__ import annotations @@ -13,195 +23,13 @@ from eosimutils.trajectory import StateSeries from eosimutils.base import ReferenceFrame from eosimutils.framegraph import FrameGraph +from eosimutils.state import CartesianState import orbitpy.orekitpropagator # triggers decorator registration from orbitpy.propagator import PropagatorFactory from orbitpy.orbits import SpaceTrackAPI, OrbitalMeanElementsMessage - -# ============================================================================= -# GLSDC IMPLEMENTATION -# ============================================================================= - -VectorFunction = Callable[[np.ndarray], np.ndarray] - - -@dataclass -class GLSDCResults: - x_hat: np.ndarray - y_hat: np.ndarray - residual: np.ndarray - jacobian: np.ndarray - normal_matrix: np.ndarray - covariance: np.ndarray - dx: np.ndarray - cost: float - iterations: int - converged: bool - x_history: list[np.ndarray] = field(default_factory=list) - cost_history: list[float] = field(default_factory=list) - - -class FiniteDifferenceJacobian: - def __init__(self, function: VectorFunction, eps: float = 1.0e-6): - self.function = function - self.eps = float(eps) - - def __call__(self, x: np.ndarray) -> np.ndarray: - x = _as_column(x, "x") - y0 = _as_column(self.function(x), "F(x)") - - ny = y0.shape[0] - nx = x.shape[0] - jac = np.empty((ny, nx), dtype=float) - - for j in range(nx): - h = self.eps * max(1.0, abs(float(x[j, 0]))) - - xp = x.copy() - xm = x.copy() - xp[j, 0] += h - xm[j, 0] -= h - - yp = _as_column(self.function(xp), "F(x + h)") - ym = _as_column(self.function(xm), "F(x - h)") - - jac[:, j] = ((yp - ym) / (2.0 * h)).ravel() - - return jac - - -class GLSDC: - def __init__(self, max_iters: int = 50, tol: float = 1.0e-10, fd_eps: float = 1.0e-6): - self.max_iters = int(max_iters) - self.tol = float(tol) - self.fd_eps = float(fd_eps) - - def solve( - self, - F: VectorFunction, - x0: np.ndarray, - y: np.ndarray, - R: Optional[np.ndarray] = None, - jacobian: Optional[VectorFunction] = None, - verbose: bool = True, - ) -> GLSDCResults: - x = _as_column(x0, "x0") - y = _as_column(y, "y") - - nx = x.shape[0] - ny = y.shape[0] - - Rinv = _inverse_covariance(R, ny) - df = jacobian if jacobian is not None else FiniteDifferenceJacobian(F, eps=self.fd_eps) - - x_history = [x.copy()] - cost_history = [] - dx = np.zeros_like(x) - converged = False - - for k in range(self.max_iters): - y_model = _as_column(F(x), "F(x)") - H = np.asarray(df(x), dtype=float) - _validate_dimensions(y_model, H, nx, ny) - - residual = y - y_model - cost = float((residual.T @ Rinv @ residual)[0, 0]) - - normal_matrix = H.T @ Rinv @ H - rhs = H.T @ Rinv @ residual - - dx = _solve_normal_equations(normal_matrix, rhs) - x = x + dx - - x_history.append(x.copy()) - cost_history.append(cost) - - dx_norm = float(np.linalg.norm(dx)) - - if verbose: - print("=" * 72) - print(f"GLSDC iteration {k + 1}") - print(f"x = {x.ravel()}") - print(f"dx = {dx.ravel()}") - print(f"|dx| = {dx_norm:.16e}") - print(f"cost = {cost:.16e}") - print(f"rms resid = {np.sqrt(np.mean(residual ** 2)):.16e}") - - if dx_norm < self.tol: - converged = True - break - - y_hat = _as_column(F(x), "F(x_hat)") - H = np.asarray(df(x), dtype=float) - _validate_dimensions(y_hat, H, nx, ny) - - residual = y - y_hat - normal_matrix = H.T @ Rinv @ H - covariance = np.linalg.pinv(normal_matrix) - final_cost = float((residual.T @ Rinv @ residual)[0, 0]) - - return GLSDCResults( - x_hat=x, - y_hat=y_hat, - residual=residual, - jacobian=H, - normal_matrix=normal_matrix, - covariance=covariance, - dx=dx, - cost=final_cost, - iterations=len(cost_history), - converged=converged, - x_history=x_history, - cost_history=cost_history, - ) - - -def _as_column(value: np.ndarray, name: str) -> np.ndarray: - arr = np.asarray(value, dtype=float) - - if arr.ndim == 0: - return arr.reshape(1, 1) - - if arr.ndim == 1: - return arr.reshape(-1, 1) - - if arr.ndim == 2 and arr.shape[1] == 1: - return arr - - raise ValueError(f"{name} must be convertible to a column vector") - - -def _inverse_covariance(R: Optional[np.ndarray], ny: int) -> np.ndarray: - if R is None: - return np.eye(ny) - - R = np.asarray(R, dtype=float) - - try: - chol = np.linalg.cholesky(R) - return np.linalg.solve(chol.T, np.linalg.solve(chol, np.eye(ny))) - except np.linalg.LinAlgError: - return np.linalg.pinv(R) - - -def _validate_dimensions(y_model: np.ndarray, H: np.ndarray, nx: int, ny: int) -> None: - if y_model.shape != (ny, 1): - raise ValueError(f"F(x) must have shape ({ny}, 1), got {y_model.shape}") - if H.shape != (ny, nx): - raise ValueError(f"Jacobian must have shape ({ny}, {nx}), got {H.shape}") - - -def _solve_normal_equations(normal_matrix: np.ndarray, rhs: np.ndarray) -> np.ndarray: - try: - return np.linalg.solve(normal_matrix, rhs) - except np.linalg.LinAlgError: - return np.linalg.pinv(normal_matrix) @ rhs - - -# ============================================================================= -# ORBIT / DATA UTILITIES -# ============================================================================= +from orbitpy.glsdc import GLSDC, FiniteDifferenceJacobian, GLSDCResults, VectorFunction def convert_datetime(np_dt): arr = np.atleast_1d(np_dt) @@ -221,13 +49,8 @@ def convert_datetime(np_dt): } ) - -# ============================================================================= -# MAIN SCRIPT -# ============================================================================= - def main(): - KM_TO_M = 1000.0 + M_TO_KM = 1.0 / 1000.0 norad_id = 41891 @@ -237,7 +60,7 @@ def main(): cyg_file = os.path.join( file_dir, "cygnss_data", - "cygnss_spacecraft_trajectory.csv", + "cygnss_spacecraft_trajectory_combined.csv", ) credentials_file = os.path.join( @@ -280,13 +103,16 @@ def main(): frame=ReferenceFrame.get("ITRF"), ) - # Transform measured/reference data to inertial frame + # Transform reference data to inertial frame registry = FrameGraph() to_frame = ReferenceFrame.get("ICRF_EC") sc_stateseries_icrf_full = registry.transform_series(sc_stateseries, to_frame) start_time = original_datearray[0] stop_time = original_datearray[-1] + # 1/2 day from start point of reference data + # This time point is used to retrieve the closest OMM from Space-Track + mid_time = original_datearray[86400*1] # ------------------------------------------------------------------------- # Build propagator @@ -303,7 +129,7 @@ def main(): # Get closest OMM from Space-Track # ------------------------------------------------------------------------- - date_str_stop = stop_time.to_dict(time_format="Gregorian_Date")["calendar_date"] + date_str_stop = mid_time.to_dict(time_format="Gregorian_Date")["calendar_date"] api = SpaceTrackAPI(credentials_file) api.login() @@ -324,28 +150,38 @@ def main(): "calendar_date": omm_dict["EPOCH"], } ) + init_time = tle_epoch omm = OrbitalMeanElementsMessage.from_dict(omm_dict) + init_state = omm + + # Uncomment to run code using initial state from cygnss data instead of SGP4 + # init_time = start_time + # init_state = CartesianState.from_array(np.concatenate((cyg_pos[0],cyg_vel[0])),init_time,"ITRF") + # init_state = registry.transform(init_state,sc_stateseries_icrf_full.frame,init_time) # ------------------------------------------------------------------------- # Define measurement epochs # ------------------------------------------------------------------------- - datearray = AbsoluteDateArray.linspace(tle_epoch, stop_time, 300) + # Downsample to 300 second timestep for measurements + datearray = AbsoluteDateArray.linspace(init_time, stop_time, 300) # Resample reference trajectory onto propagation epochs sc_stateseries_icrf = sc_stateseries_icrf_full.resample(datearray) - # Reference positions: shape (M, 3) + # Reference positions: shape (M, 3), where M is number of measurements y_reference_positions = sc_stateseries_icrf.data[0] - # GLSDC requires column vector: shape (3M, 1) + # GLSDC code requires column vector: shape (3M, 1) y = y_reference_positions.reshape(-1, 1) # ------------------------------------------------------------------------- # Define measurement model F(x) # ------------------------------------------------------------------------- + # Function which maps solved-for variable x to the measurements + # which will result from that value of x def F(x: np.ndarray) -> np.ndarray: drag_coeff = float(np.asarray(x).reshape(-1)[0]) @@ -353,7 +189,7 @@ def F(x: np.ndarray) -> np.ndarray: propagated_state_series = prop.execute_2( times=datearray, - initial_state=omm, + initial_state=init_state, ) propagated_positions = propagated_state_series.data[0] @@ -364,12 +200,13 @@ def F(x: np.ndarray) -> np.ndarray: # Solve for drag coefficient # ------------------------------------------------------------------------- - x0 = np.array([[300.2]]) + # Initial guess for drag coeff + x0 = np.array([[300.0]]) solver = GLSDC( - max_iters=3, - tol=1.0e-8, - fd_eps=1.0e-2, + max_iters=4, + tol=1.0e-2, + fd_eps=1.0e-4, ) results = solver.solve( @@ -390,7 +227,7 @@ def F(x: np.ndarray) -> np.ndarray: print(f"Converged: {results.converged}") print(f"Iterations: {results.iterations}") print(f"Estimated drag coefficient: {cd_hat:.16e}") - print(f"1-sigma formal uncertainty: {cd_sigma:.16e}") + print(f"1-sigma uncertainty: {cd_sigma:.16e}") print(f"Final cost: {results.cost:.16e}") print(f"Final RMS position resid: {np.sqrt(np.mean(results.residual ** 2)):.16e} km") @@ -430,6 +267,5 @@ def F(x: np.ndarray) -> np.ndarray: plt.show() - if __name__ == "__main__": main() \ No newline at end of file diff --git a/examples/solve_drag_coefficient_x0.py b/examples/solve_drag_coefficient_x0.py new file mode 100644 index 0000000..a96145f --- /dev/null +++ b/examples/solve_drag_coefficient_x0.py @@ -0,0 +1,331 @@ +"""Estimate CYGNSS drag coefficient and initial Cartesian state with GLSDC. + +Similar to the example solve_drag_coefficient, except it initializes the +state using the cygnss data rather than SGP4, and also includes the initial +state as a solved-for variable. Hence it achieves a very low position +error over the course of the 3-day arc (max ~.06 km) and a positive drag coefficient +(1.41). + +""" + +from __future__ import annotations + +import os +import numpy as np +import matplotlib.pyplot as plt + +from eosimutils.time import AbsoluteDateArray +from eosimutils.trajectory import StateSeries +from eosimutils.base import ReferenceFrame +from eosimutils.framegraph import FrameGraph +from eosimutils.state import CartesianState + +import orbitpy.orekitpropagator # triggers decorator registration +from orbitpy.propagator import PropagatorFactory +from orbitpy.glsdc import GLSDC + + +def convert_datetime(np_dt): + arr = np.atleast_1d(np_dt) + + if not np.issubdtype(arr.dtype, np.datetime64): + raise TypeError("np_dt must be a numpy.datetime64 scalar or array") + + arr_ms = arr.astype("datetime64[ms]") + iso = np.datetime_as_string(arr_ms, unit="ms", timezone="naive") + iso_list = [str(iso)] if np.isscalar(iso) else iso.astype(str).tolist() + + return AbsoluteDateArray.from_dict( + { + "time_format": "GREGORIAN_DATE", + "calendar_date": iso_list, + "time_scale": "UTC", + } + ) + + +def main(): + M_TO_KM = 1.0 / 1000.0 + + file_dir = os.path.dirname(os.path.abspath(__file__)) + + cyg_file = os.path.join( + file_dir, + "cygnss_data", + "cygnss_spacecraft_trajectory_combined.csv", + ) + + # ------------------------------------------------------------------------- + # Read CYGNSS reference trajectory + # ------------------------------------------------------------------------- + + cyg_pos = np.loadtxt( + cyg_file, + delimiter=",", + skiprows=1, + usecols=(1, 2, 3), + ) * M_TO_KM + + cyg_vel = np.loadtxt( + cyg_file, + delimiter=",", + skiprows=1, + usecols=(4, 5, 6), + ) * M_TO_KM + + ddm_timestamp_utc = np.loadtxt( + cyg_file, + delimiter=",", + skiprows=1, + usecols=(0,), + dtype="datetime64[ns]", + ) + + original_datearray = convert_datetime(ddm_timestamp_utc) + + sc_stateseries_itrf = StateSeries( + time=original_datearray, + data=[cyg_pos, cyg_vel], + frame=ReferenceFrame.get("ITRF"), + ) + + # Transform reference trajectory to inertial frame + registry = FrameGraph() + inertial_frame = ReferenceFrame.get("ICRF_EC") + + sc_stateseries_icrf_full = registry.transform_series( + sc_stateseries_itrf, + inertial_frame, + ) + + init_time = original_datearray[0] + stop_time = original_datearray[-1] + + # ------------------------------------------------------------------------- + # Build nominal initial state from the first reference state + # ------------------------------------------------------------------------- + + init_state_itrf = CartesianState.from_array( + np.concatenate((cyg_pos[0], cyg_vel[0])), + init_time, + "ITRF", + ) + + init_state_icrf = registry.transform( + init_state_itrf, + inertial_frame, + init_time, + ) + + r0_nominal = init_state_icrf.position.to_numpy() + v0_nominal = init_state_icrf.velocity.to_numpy() + + # ------------------------------------------------------------------------- + # Build propagator + # ------------------------------------------------------------------------- + + specs = { + "propagator_type": "OREKIT_PROPAGATOR", + "step_size": 10, + "drag_coeff": 2.2 + } + + prop = PropagatorFactory.from_dict(specs) + + # ------------------------------------------------------------------------- + # Measurement epochs and measurements + # ------------------------------------------------------------------------- + + datearray = AbsoluteDateArray.linspace(init_time, stop_time, 300) + + sc_stateseries_icrf = sc_stateseries_icrf_full.resample(datearray) + + y_reference_positions = sc_stateseries_icrf.data[0] + y = y_reference_positions.reshape(-1, 1) + + def F(x: np.ndarray) -> np.ndarray: + x = np.asarray(x, dtype=float).reshape(-1) + + drag_coeff = float(x[0]) + r0 = x[1:4] + v0 = x[4:7] + + prop.set_drag_coeff(drag_coeff) + + initial_state = CartesianState.from_array( + np.concatenate((r0, v0)), + init_time, + "ICRF_EC", + ) + + propagated_state_series = prop.execute_2( + times=datearray, + initial_state=initial_state, + ) + + propagated_positions = propagated_state_series.data[0] + + return propagated_positions.reshape(-1, 1) + + # ------------------------------------------------------------------------- + # Initial guess + # ------------------------------------------------------------------------- + + cd0 = 2.2 + + x0 = np.array( + [ + cd0, + r0_nominal[0], + r0_nominal[1], + r0_nominal[2], + v0_nominal[0], + v0_nominal[1], + v0_nominal[2], + ], + dtype=float, + ).reshape(-1, 1) + + # Scale used only internally by GLSDC. + # + # Characteristic magnitudes for each solve variable: + # Cd scale: 1 + # position scale: 1 km + # velocity scale: 1e-3 km/s = 1 m/s + # The measurement model still receives x in physical units. + x_scale = np.array( + [ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0e-3, + 1.0e-3, + 1.0e-3, + ], + dtype=float, + ).reshape(-1, 1) + + solver = GLSDC( + max_iters=5, + tol=1.0e-3, + fd_eps=1.0e-4, + x_scale=x_scale, + ) + + results = solver.solve( + F=F, + x0=x0, + y=y, + R=None, + verbose=True, + ) + + x_hat = results.x_hat.reshape(-1) + + cd_hat = x_hat[0] + r0_hat = x_hat[1:4] + v0_hat = x_hat[4:7] + + dr_hat = r0_hat - r0_nominal + dv_hat = v0_hat - v0_nominal + + cd_sigma = np.sqrt(results.covariance[0, 0]) + state_sigma = np.sqrt(np.diag(results.covariance)) + + print() + print("FINAL RESULTS") + print("=" * 80) + print(f"Converged: {results.converged}") + print(f"Iterations: {results.iterations}") + print(f"Estimated drag coefficient: {cd_hat:.16e}") + print(f"Cd 1-sigma uncertainty: {cd_sigma:.16e}") + print(f"Final cost: {results.cost:.16e}") + print(f"Final RMS position residual: {np.sqrt(np.mean(results.residual ** 2)):.16e} km") + + print() + print("Estimated initial state correction:") + print(f"dr_x: {dr_hat[0]: .16e} km") + print(f"dr_y: {dr_hat[1]: .16e} km") + print(f"dr_z: {dr_hat[2]: .16e} km") + print(f"dv_x: {dv_hat[0]: .16e} km/s") + print(f"dv_y: {dv_hat[1]: .16e} km/s") + print(f"dv_z: {dv_hat[2]: .16e} km/s") + + print() + print("Estimated initial state:") + print(f"r0_x: {r0_hat[0]: .16e} km") + print(f"r0_y: {r0_hat[1]: .16e} km") + print(f"r0_z: {r0_hat[2]: .16e} km") + print(f"v0_x: {v0_hat[0]: .16e} km/s") + print(f"v0_y: {v0_hat[1]: .16e} km/s") + print(f"v0_z: {v0_hat[2]: .16e} km/s") + + print() + print("1-sigma uncertainties:") + print(f"sigma Cd: {state_sigma[0]: .16e}") + print(f"sigma r0_x: {state_sigma[1]: .16e} km") + print(f"sigma r0_y: {state_sigma[2]: .16e} km") + print(f"sigma r0_z: {state_sigma[3]: .16e} km") + print(f"sigma v0_x: {state_sigma[4]: .16e} km/s") + print(f"sigma v0_y: {state_sigma[5]: .16e} km/s") + print(f"sigma v0_z: {state_sigma[6]: .16e} km/s") + + # ------------------------------------------------------------------------- + # Plot final residuals + # ------------------------------------------------------------------------- + + final_prop_positions = results.y_hat.reshape((-1, 3)) + diff_pos = final_prop_positions - y_reference_positions + error_norm = np.linalg.norm(diff_pos, axis=1) + + time_days = datearray.ephemeris_time * (1.0 / 86400.0) + + plt.figure() + plt.plot(time_days, diff_pos[:, 0], label="X") + plt.plot(time_days, diff_pos[:, 1], label="Y") + plt.plot(time_days, diff_pos[:, 2], label="Z") + plt.xlabel("Ephemeris Time (days)") + plt.ylabel("Position Residual (km)") + plt.title(f"Final Position Residuals, Cd = {cd_hat:.6f}") + plt.legend() + plt.grid(True) + + plt.figure() + plt.plot(time_days, error_norm) + plt.xlabel("Ephemeris Time (days)") + plt.ylabel("Position Error Norm (km)") + plt.title("Final Position Error Norm") + plt.grid(True) + + plt.figure() + plt.plot([float(x[0, 0]) for x in results.x_history], marker="o") + plt.xlabel("Iteration") + plt.ylabel("Drag Coefficient") + plt.title("Drag Coefficient Convergence") + plt.grid(True) + + plt.figure() + plt.plot([float(x[1, 0] - r0_nominal[0]) for x in results.x_history], label="dr_x") + plt.plot([float(x[2, 0] - r0_nominal[1]) for x in results.x_history], label="dr_y") + plt.plot([float(x[3, 0] - r0_nominal[2]) for x in results.x_history], label="dr_z") + plt.xlabel("Iteration") + plt.ylabel("Initial Position Correction (km)") + plt.title("Initial Position Correction Convergence") + plt.legend() + plt.grid(True) + + plt.figure() + plt.plot([float(x[4, 0] - v0_nominal[0]) for x in results.x_history], label="dv_x") + plt.plot([float(x[5, 0] - v0_nominal[1]) for x in results.x_history], label="dv_y") + plt.plot([float(x[6, 0] - v0_nominal[2]) for x in results.x_history], label="dv_z") + plt.xlabel("Iteration") + plt.ylabel("Initial Velocity Correction (km/s)") + plt.title("Initial Velocity Correction Convergence") + plt.legend() + plt.grid(True) + + plt.show() + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/orbitpy/glsdc.py b/orbitpy/glsdc.py new file mode 100644 index 0000000..19f77e3 --- /dev/null +++ b/orbitpy/glsdc.py @@ -0,0 +1,412 @@ +""" +.. module:: orbitpy.glsdc + :synopsis: Generalized least-squares differential correction. + +The GLSDC module provides classes and functions for solving nonlinear least-squares +problems using differential correction. + +It solves problems of the form + + minimize (y - F(x))^T R^{-1} (y - F(x)) + +where ``y`` is the measurement vector, ``F`` is the measurement model, ``x`` is +the solve-for parameter vector, and ``R`` is the measurement covariance matrix. + +Classes: + +- GLSDC: Solves nonlinear least-squares problems using iterative differential + correction. + +- FiniteDifferenceJacobian: Computes a central finite-difference Jacobian for a + vector-valued measurement model. + +**Example:** + + solver = GLSDC(max_iters=20, tol=1.0e-10, fd_eps=1.0e-6) + results = solver.solve(F=measurement_model, x0=x0, y=measurements) +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Callable, Optional + +import numpy as np + +VectorFunction = Callable[[np.ndarray], np.ndarray] + +@dataclass +class GLSDCResults: + """Container for GLSDC solution results. + + Args: + x_hat (np.ndarray): Estimated parameter vector in physical units. + y_hat (np.ndarray): Estimated measurement vector, ``F(x_hat)``. + residual (np.ndarray): Final measurement residual, ``y - F(x_hat)``. + jacobian (np.ndarray): Final Jacobian with respect to physical parameters. + normal_matrix (np.ndarray): Final normal matrix in physical parameters. + covariance (np.ndarray): Pseudo-inverse of the final normal matrix. + dx (np.ndarray): Final parameter correction in physical units. + cost (float): Final weighted least-squares cost. + iterations (int): Number of completed iterations. + converged (bool): Flag indicating whether the convergence tolerance was met. + x_history (list[np.ndarray]): Parameter estimate history in physical units. + cost_history (list[float]): Cost history. + """ + + x_hat: np.ndarray + y_hat: np.ndarray + residual: np.ndarray + jacobian: np.ndarray + normal_matrix: np.ndarray + covariance: np.ndarray + dx: np.ndarray + cost: float + iterations: int + converged: bool + x_history: list[np.ndarray] = field(default_factory=list) + cost_history: list[float] = field(default_factory=list) + + +class FiniteDifferenceJacobian: + """Computes a central finite-difference Jacobian. + + Args: + function (VectorFunction): Vector-valued function to differentiate. + eps (float): Relative finite-difference step size. + + Raises: + ValueError: If ``eps`` is not positive and finite. + """ + + def __init__(self, function: VectorFunction, eps: float = 1.0e-6) -> None: + if not np.isfinite(eps) or eps <= 0.0: + raise ValueError("eps must be positive and finite.") + + self.function = function + self.eps = float(eps) + + def __call__(self, x: np.ndarray) -> np.ndarray: + """Evaluate the finite-difference Jacobian. + + Args: + x (np.ndarray): Parameter vector. + + Returns: + np.ndarray: Jacobian matrix with shape ``(ny, nx)``. + + """ + x = _as_column(x, "x") + y0 = _as_column(self.function(x), "F(x)") + + ny = y0.shape[0] + nx = x.shape[0] + jacobian = np.empty((ny, nx), dtype=float) + + for j in range(nx): + step = self.eps * max(1.0, abs(float(x[j, 0]))) + + xp = x.copy() + xm = x.copy() + + xp[j, 0] += step + xm[j, 0] -= step + + yp = _as_column(self.function(xp), "F(x + h)") + ym = _as_column(self.function(xm), "F(x - h)") + + jacobian[:, j] = ((yp - ym) / (2.0 * step)).ravel() + + return jacobian + + +class GLSDC: + """Generalized least-squares differential-correction solver. + + Args: + max_iters (int): Maximum number of differential-correction iterations. + tol (float): Convergence tolerance on the parameter correction norm. + fd_eps (float): Relative finite-difference step size. + x_scale (Optional[np.ndarray]): Optional parameter scale vector. If provided, + it must have the same dimension as ``x0`` passed to :meth:`solve`. + Scaling is used internally only; the measurement model receives physical + parameters. + + Raises: + ValueError: If solver options are invalid. + """ + + def __init__( + self, + max_iters: int = 50, + tol: float = 1.0e-10, + fd_eps: float = 1.0e-6, + x_scale: Optional[np.ndarray] = None, + ) -> None: + if max_iters <= 0: + raise ValueError("max_iters must be positive.") + if not np.isfinite(tol) or tol <= 0.0: + raise ValueError("tol must be positive and finite.") + if not np.isfinite(fd_eps) or fd_eps <= 0.0: + raise ValueError("fd_eps must be positive and finite.") + + self.max_iters = int(max_iters) + self.tol = float(tol) + self.fd_eps = float(fd_eps) + self.x_scale = ( + None if x_scale is None else _as_column(x_scale, "x_scale") + ) + + if self.x_scale is not None: + if ( + np.any(~np.isfinite(self.x_scale)) + or np.any(self.x_scale <= 0.0) + ): + raise ValueError("x_scale entries must be positive and finite.") + + def solve( + self, + F: VectorFunction, + x0: np.ndarray, + y: np.ndarray, + R: Optional[np.ndarray] = None, + jacobian: Optional[VectorFunction] = None, + verbose: bool = True, + ) -> GLSDCResults: + """Solve a nonlinear least-squares problem. + + Args: + F (VectorFunction): Measurement model. The function must accept a + physical parameter vector ``x`` and return a measurement vector. + x0 (np.ndarray): Initial parameter estimate. + y (np.ndarray): Measurement vector. + R (Optional[np.ndarray]): Measurement covariance matrix. If ``None``, + identity covariance is used. + jacobian (Optional[VectorFunction]): Optional analytic or user-supplied + Jacobian with respect to physical parameters. If ``None``, a central + finite-difference Jacobian is used. + verbose (bool): If ``True``, print iteration information. + + Returns: + GLSDCResults: Solver results. + + Raises: + ValueError: If dimensions are inconsistent. + """ + x0 = _as_column(x0, "x0") + y = _as_column(y, "y") + + nx = x0.shape[0] + ny = y.shape[0] + + x_scale = self._get_x_scale(nx) + z = x0 / x_scale + + def F_scaled(z_in: np.ndarray) -> np.ndarray: + x_physical = _as_column(z_in, "z") * x_scale + return F(x_physical) + + if jacobian is None: + df_scaled = FiniteDifferenceJacobian( + F_scaled, eps=self.fd_eps + ) + else: + + def df_scaled(z_in: np.ndarray) -> np.ndarray: + x_physical = _as_column(z_in, "z") * x_scale + H_x = np.asarray(jacobian(x_physical), dtype=float) + return H_x @ np.diagflat(x_scale) + + Rinv = _inverse_covariance(R, ny) + + x_history = [(z * x_scale).copy()] + cost_history: list[float] = [] + + dz = np.zeros_like(z) + converged = False + + for k in range(self.max_iters): + y_model = _as_column(F_scaled(z), "F(x)") + H_z = np.asarray(df_scaled(z), dtype=float) + _validate_dimensions(y_model, H_z, nx, ny) + + residual = y - y_model + cost = float((residual.T @ Rinv @ residual)[0, 0]) + + normal_matrix_z = H_z.T @ Rinv @ H_z + rhs_z = H_z.T @ Rinv @ residual + + dz = _solve_normal_equations(normal_matrix_z, rhs_z) + z = z + dz + + x = z * x_scale + dx = dz * x_scale + + x_history.append(x.copy()) + cost_history.append(cost) + + dx_norm = float(np.linalg.norm(dx)) + + if verbose: + print("=" * 72) + print(f"GLSDC iteration {k + 1}") + print(f"x = {x.ravel()}") + print(f"dx = {dx.ravel()}") + print(f"|dx| = {dx_norm:.16e}") + print(f"cost = {cost:.16e}") + print(f"rms resid = {np.sqrt(np.mean(residual ** 2)):.16e}") + + if dx_norm < self.tol: + converged = True + break + + x_hat = z * x_scale + y_hat = _as_column(F(x_hat), "F(x_hat)") + + H_z = np.asarray(df_scaled(z), dtype=float) + H_x = H_z @ np.diagflat(1.0 / x_scale) + _validate_dimensions(y_hat, H_x, nx, ny) + + residual = y - y_hat + normal_matrix_x = H_x.T @ Rinv @ H_x + covariance_x = np.linalg.pinv(normal_matrix_x) + + final_cost = float((residual.T @ Rinv @ residual)[0, 0]) + dx_final = dz * x_scale + + return GLSDCResults( + x_hat=x_hat, + y_hat=y_hat, + residual=residual, + jacobian=H_x, + normal_matrix=normal_matrix_x, + covariance=covariance_x, + dx=dx_final, + cost=final_cost, + iterations=len(cost_history), + converged=converged, + x_history=x_history, + cost_history=cost_history, + ) + + def _get_x_scale(self, nx: int) -> np.ndarray: + """Get the internal parameter scale vector. + + Args: + nx (int): Number of solve-for parameters. + + Returns: + np.ndarray: Parameter scale vector with shape ``(nx, 1)``. + + Raises: + ValueError: If ``x_scale`` has the wrong shape. + """ + if self.x_scale is None: + return np.ones((nx, 1), dtype=float) + + if self.x_scale.shape != (nx, 1): + raise ValueError( + f"x_scale must have shape ({nx}, 1), " + f"got {self.x_scale.shape}." + ) + + return self.x_scale.copy() + + +def _as_column(value: np.ndarray, name: str) -> np.ndarray: + """Convert an input value to a column vector. + + Args: + value (np.ndarray): Input value. + name (str): Name used in error messages. + + Returns: + np.ndarray: Column vector. + + Raises: + ValueError: If the input cannot be converted to a column vector. + """ + arr = np.asarray(value, dtype=float) + + if arr.ndim == 0: + arr = arr.reshape(1, 1) + elif arr.ndim == 1: + arr = arr.reshape(-1, 1) + elif arr.ndim == 2 and arr.shape[1] == 1: + pass + else: + raise ValueError(f"{name} must be convertible to a column vector.") + + return arr + + +def _inverse_covariance(R: Optional[np.ndarray], ny: int) -> np.ndarray: + """Compute the inverse measurement covariance matrix. + + Args: + R (Optional[np.ndarray]): Measurement covariance matrix. If ``None``, + identity covariance is used. + ny (int): Number of measurements. + + Returns: + np.ndarray: Inverse covariance matrix. + + Raises: + ValueError: If ``R`` has an invalid shape. + """ + if R is None: + return np.eye(ny) + + R = np.asarray(R, dtype=float) + + if R.shape != (ny, ny): + raise ValueError(f"R must have shape ({ny}, {ny}).") + + try: + chol = np.linalg.cholesky(R) + return np.linalg.solve(chol.T, np.linalg.solve(chol, np.eye(ny))) + except np.linalg.LinAlgError: + return np.linalg.pinv(R) + + +def _validate_dimensions( + y_model: np.ndarray, + H: np.ndarray, + nx: int, + ny: int, +) -> None: + """Validate measurement-model and Jacobian dimensions. + + Args: + y_model (np.ndarray): Measurement-model output. + H (np.ndarray): Jacobian matrix. + nx (int): Number of solve-for parameters. + ny (int): Number of measurements. + + Raises: + ValueError: If dimensions are inconsistent. + """ + if y_model.shape != (ny, 1): + raise ValueError(f"F(x) must have shape ({ny}, 1), got {y_model.shape}.") + + if H.shape != (ny, nx): + raise ValueError(f"Jacobian must have shape ({ny}, {nx}), got {H.shape}.") + + +def _solve_normal_equations( + normal_matrix: np.ndarray, + rhs: np.ndarray, +) -> np.ndarray: + """Solve the normal equations. + + Args: + normal_matrix (np.ndarray): Normal matrix. + rhs (np.ndarray): Right-hand side vector. + + Returns: + np.ndarray: Parameter correction vector. + """ + try: + return np.linalg.solve(normal_matrix, rhs) + except np.linalg.LinAlgError: + return np.linalg.pinv(normal_matrix) @ rhs \ No newline at end of file From bc4ca2f482a08045c64db56ef57c474b8a3b91ac Mon Sep 17 00:00:00 2001 From: Ryan Ketzner Date: Tue, 14 Jul 2026 00:27:28 -0700 Subject: [PATCH 5/7] Updated solve_drag_coefficient_x0 to split data. --- examples/solve_drag_coefficient_x0.py | 47 ++++++++++++++++++++++----- 1 file changed, 38 insertions(+), 9 deletions(-) diff --git a/examples/solve_drag_coefficient_x0.py b/examples/solve_drag_coefficient_x0.py index a96145f..0eba4c1 100644 --- a/examples/solve_drag_coefficient_x0.py +++ b/examples/solve_drag_coefficient_x0.py @@ -1,10 +1,10 @@ """Estimate CYGNSS drag coefficient and initial Cartesian state with GLSDC. -Similar to the example solve_drag_coefficient, except it initializes the +Similar to the example solve_drag_coefficient, except it initializes the state using the cygnss data rather than SGP4, and also includes the initial -state as a solved-for variable. Hence it achieves a very low position -error over the course of the 3-day arc (max ~.06 km) and a positive drag coefficient -(1.41). +state as a solved-for variable. Only the first half of the 3-day arc is used for +orbit determination, and the resulting trajectory is evaluated over the full +arc. """ @@ -137,11 +137,13 @@ def main(): # ------------------------------------------------------------------------- datearray = AbsoluteDateArray.linspace(init_time, stop_time, 300) + fit_datearray = datearray[: len(datearray) // 2] sc_stateseries_icrf = sc_stateseries_icrf_full.resample(datearray) y_reference_positions = sc_stateseries_icrf.data[0] - y = y_reference_positions.reshape(-1, 1) + y_fit_positions = y_reference_positions[: len(fit_datearray)] + y = y_fit_positions.reshape(-1, 1) def F(x: np.ndarray) -> np.ndarray: x = np.asarray(x, dtype=float).reshape(-1) @@ -159,7 +161,7 @@ def F(x: np.ndarray) -> np.ndarray: ) propagated_state_series = prop.execute_2( - times=datearray, + times=fit_datearray, initial_state=initial_state, ) @@ -223,7 +225,7 @@ def F(x: np.ndarray) -> np.ndarray: x_hat = results.x_hat.reshape(-1) - cd_hat = x_hat[0] + cd_hat = float(x_hat[0]) r0_hat = x_hat[1:4] v0_hat = x_hat[4:7] @@ -275,16 +277,36 @@ def F(x: np.ndarray) -> np.ndarray: # Plot final residuals # ------------------------------------------------------------------------- - final_prop_positions = results.y_hat.reshape((-1, 3)) + prop.set_drag_coeff(cd_hat) + + estimated_initial_state = CartesianState.from_array( + np.concatenate((r0_hat, v0_hat)), + init_time, + "ICRF_EC", + ) + + final_state_series = prop.execute_2( + times=datearray, + initial_state=estimated_initial_state, + ) + + final_prop_positions = final_state_series.data[0] diff_pos = final_prop_positions - y_reference_positions error_norm = np.linalg.norm(diff_pos, axis=1) time_days = datearray.ephemeris_time * (1.0 / 86400.0) + fit_end_day = fit_datearray.ephemeris_time[-1] * (1.0 / 86400.0) plt.figure() plt.plot(time_days, diff_pos[:, 0], label="X") plt.plot(time_days, diff_pos[:, 1], label="Y") plt.plot(time_days, diff_pos[:, 2], label="Z") + plt.axvline( + fit_end_day, + color="black", + linestyle="--", + label="End of fit data", + ) plt.xlabel("Ephemeris Time (days)") plt.ylabel("Position Residual (km)") plt.title(f"Final Position Residuals, Cd = {cd_hat:.6f}") @@ -293,9 +315,16 @@ def F(x: np.ndarray) -> np.ndarray: plt.figure() plt.plot(time_days, error_norm) + plt.axvline( + fit_end_day, + color="black", + linestyle="--", + label="End of fit data", + ) plt.xlabel("Ephemeris Time (days)") plt.ylabel("Position Error Norm (km)") plt.title("Final Position Error Norm") + plt.legend() plt.grid(True) plt.figure() @@ -328,4 +357,4 @@ def F(x: np.ndarray) -> np.ndarray: plt.show() if __name__ == "__main__": - main() \ No newline at end of file + main() From d6874486b5bebee89aa01d41f519e438d103e7ce Mon Sep 17 00:00:00 2001 From: Ryan Ketzner Date: Sun, 26 Jul 2026 14:10:31 -0700 Subject: [PATCH 6/7] Fixed bug in CoverageKinematics. --- extern/CoverageKinematics | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/extern/CoverageKinematics b/extern/CoverageKinematics index c547826..399c827 160000 --- a/extern/CoverageKinematics +++ b/extern/CoverageKinematics @@ -1 +1 @@ -Subproject commit c54782692df6db8a828aaa5d1ee4cb12aaf67ab6 +Subproject commit 399c827a3a33bee3f36db68143f60397b53e55b2 From e885fc4841665676ae70ade4bbe31fe47a57a7b4 Mon Sep 17 00:00:00 2001 From: Ryan Ketzner Date: Sun, 26 Jul 2026 15:33:42 -0700 Subject: [PATCH 7/7] Deleted old intermediate progress files. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Deleted: examples/specular_validation_prop.py: This was added in an intermediate state in an old commit but never finished. Removed. examples/orbit_estimation.py: Not sure what this file was for, but it isn’t finished. examples/propagator_validation.py: Compares error for two propagators. --- examples/orbit_estimation.py | 168 -------------------- examples/propagator_validation.py | 227 --------------------------- examples/specular_validation_prop.py | 199 ----------------------- 3 files changed, 594 deletions(-) delete mode 100644 examples/orbit_estimation.py delete mode 100644 examples/propagator_validation.py delete mode 100644 examples/specular_validation_prop.py diff --git a/examples/orbit_estimation.py b/examples/orbit_estimation.py deleted file mode 100644 index 85f01ba..0000000 --- a/examples/orbit_estimation.py +++ /dev/null @@ -1,168 +0,0 @@ -import numpy as np -import os -import matplotlib.pyplot as plt - -from eosimutils.time import AbsoluteDate, AbsoluteDateArray -from eosimutils.trajectory import StateSeries, PositionSeries -from eosimutils.base import ReferenceFrame, SurfaceType -from eosimutils.state import CartesianState -from eosimutils.framegraph import FrameGraph - - -from orbitpy.specular import get_specular_trajectory, get_topk_trajectories - -import orbitpy.orekitpropagator # triggers decorator registration -from orbitpy.propagator import PropagatorFactory -from orbitpy.orbits import TwoLineElementSet, SpaceTrackAPI, OrbitalMeanElementsMessage - -def convert_datetime(np_dt): - """Convert numpy datetime object to AbsoluteDateArray - - Args: - np_dt (np.ndarray): Numpy datetime64 array. - - Returns: - AbsoluteDateArray: Orbitpy AbsoluteDateArray object with the times from the datetime object - """ - - arr = np.atleast_1d(np_dt) - - if not np.issubdtype(arr.dtype, np.datetime64): - raise TypeError("np_dt must be a numpy.datetime64 scalar or array") - - # Convert to millisecond precision ISO strings without timezone - arr_ms = arr.astype("datetime64[ms]") - iso = np.datetime_as_string(arr_ms, unit="ms", timezone="naive") - iso_list = [str(iso)] if np.isscalar(iso) else iso.astype(str).tolist() - - return AbsoluteDateArray.from_dict( - { - "time_format": "GREGORIAN_DATE", - "calendar_date": iso_list, - "time_scale": "UTC", - } - ) - -# ------------- -# READ IN DATA -# ------------- - -KM_TO_M = 1000.0 -M_TO_KM = 1.0 / 1000.0 - -sat_number = "03" -ddm_num = 1 -folder = os.path.join( - os.path.dirname(os.path.abspath(__file__)), "cygnss_data", "" -) -ext = ".npy" - -cyg_file = folder + "cygnss_spacecraft_trajectory.csv" -cyg_pos = data = np.loadtxt( - cyg_file, - delimiter=",", - skiprows=1, - usecols=(1, 2, 3) -)*M_TO_KM -cyg_vel = data = np.loadtxt( - cyg_file, - delimiter=",", - skiprows=1, - usecols=(4, 5, 6) -)*M_TO_KM - -ddm_timestamp_utc = np.loadtxt( - cyg_file, - delimiter=",", - skiprows=1, - usecols=(0,), - dtype="datetime64[ns]" -) - -norad_id = 41891 - -# ----------------------------------- -# Process input into orbitpy objects -# ----------------------------------- - -# Create AbsoluteDateArray from ddm timestamps -datearray = convert_datetime(ddm_timestamp_utc) - -sc_stateseries = StateSeries( - time=datearray, data=[cyg_pos, cyg_vel], frame=ReferenceFrame.get("ITRF") -) - - -# Transform reference data to inertial frame -registry = FrameGraph() -to_frame = ReferenceFrame.get("ICRF_EC") -sc_stateseries_icrf = registry.transform_series(sc_stateseries, to_frame) - -# Start and stop time of reference data -start_time = datearray[0] -stop_time = datearray[-1] - -# Setup propagator -specs = { - "propagator_type": "OREKIT_PROPAGATOR", - "stepSize": 10, -} -prop = PropagatorFactory.from_dict(specs) -prop.set_drag_coeff(0.0) - -# Use spacetrack to get initial tle -# Convert AbsoluteDate to ISO 8601 string format -date_str_start = start_time.to_dict(time_format="Gregorian_Date")["calendar_date"] -date_str_stop = stop_time.to_dict(time_format="Gregorian_Date")["calendar_date"] -file_dir = os.path.dirname(__file__) -api = SpaceTrackAPI(os.path.join(file_dir, "spacetrack/credentials.json")) -api.login() -omm_dict = api.get_closest_omm( - norad_id=norad_id, # NORAD catalog ID of your satellite - target_date_time=date_str_stop, - within_days=1 # Optional: max days before target to search -) - -# Extract the TLE and time from the OMM -if omm_dict: - tle_epoch = AbsoluteDate.from_dict({"time_scale" : omm_dict["TIME_SYSTEM"], "time_format" : "GREGORIAN_DATE", "calendar_date" : omm_dict["EPOCH"]}) - omm = OrbitalMeanElementsMessage.from_dict(omm_dict) - -# Create array of dates starting at TLE epoch and continuing until end of reference trajectory, -# with a 300 second step size (5 min) -datearray = AbsoluteDateArray.linspace(tle_epoch, stop_time, 300) - -omm = sc_stateseries_icrf.at(tle_epoch) -omm = np.hstack((omm[0][0], omm[1][0])) -omm = CartesianState.from_array(omm, frame=ReferenceFrame.get("ICRF_EC"), time=tle_epoch) -# Propagate starting from TLE to each of the time points in the date array -sat_state_series_sgp4 = prop.execute_2(times=datearray, initial_state=omm) - -# Resample reference trajectory onto these time points. -sc_stateseries_icrf = sc_stateseries_icrf.resample(datearray) - -# Take difference between propagated and reference states -diff_st = sat_state_series_sgp4 - sc_stateseries_icrf - -# -------------------------- -# Plot results -# -------------------------- - -# Plot individual error components -plt.figure() -plt.plot(sat_state_series_sgp4.time.ephemeris_time*(1/86400.0), diff_st.data[0][:, 0], label="Propagated X") -plt.plot(sat_state_series_sgp4.time.ephemeris_time*(1/86400.0), diff_st.data[0][:, 1], label="Propagated Y") -plt.plot(sat_state_series_sgp4.time.ephemeris_time*(1/86400.0), diff_st.data[0][:, 2], label="Propagated Z") -plt.xlabel("Ephemeris Time (days)") -plt.ylabel("Position (km)") -plt.title("Propagated Satellite Position Over Time") -plt.legend() - -# Plot error norm -plt.figure() -error_norm = np.linalg.norm(diff_st.data[0], axis=1) -plt.plot(sat_state_series_sgp4.time.ephemeris_time*(1/86400.0), error_norm) -plt.xlabel("Ephemeris Time (days)") -plt.ylabel("Position Error Norm (km)") -plt.title("Norm of Position Error Between Propagated State and Reference State") -plt.show() \ No newline at end of file diff --git a/examples/propagator_validation.py b/examples/propagator_validation.py deleted file mode 100644 index b7e18c3..0000000 --- a/examples/propagator_validation.py +++ /dev/null @@ -1,227 +0,0 @@ -import numpy as np -import os -import matplotlib.pyplot as plt - -from eosimutils.time import AbsoluteDateArray -from eosimutils.trajectory import StateSeries, PositionSeries -from eosimutils.base import ReferenceFrame, SurfaceType -from eosimutils.state import CartesianState -from eosimutils.framegraph import FrameGraph - - -from orbitpy.specular import get_specular_trajectory, get_topk_trajectories - -import orbitpy.orekitpropagator # triggers decorator registration -from orbitpy.propagator import PropagatorFactory -from orbitpy.orbits import TwoLineElementSet, SpaceTrackAPI, OrbitalMeanElementsMessage - -def get_first_idx(arr): - """Get index of first non-naan row in numpy array""" - - mask = ~np.isnan(arr).all(axis=1) - first_idx = np.argmax(mask) - return first_idx - -def convert_datetime(np_dt): - """Convert numpy datetime object to AbsoluteDateArray - - Args: - np_dt (np.ndarray): Numpy datetime64 array. - - Returns: - AbsoluteDateArray: Orbitpy AbsoluteDateArray object with the times from the datetime object - """ - - arr = np.atleast_1d(np_dt) - - if not np.issubdtype(arr.dtype, np.datetime64): - raise TypeError("np_dt must be a numpy.datetime64 scalar or array") - - # Convert to millisecond precision ISO strings without timezone - arr_ms = arr.astype("datetime64[ms]") - iso = np.datetime_as_string(arr_ms, unit="ms", timezone="naive") - iso_list = [str(iso)] if np.isscalar(iso) else iso.astype(str).tolist() - - return AbsoluteDateArray.from_dict( - { - "time_format": "GREGORIAN_DATE", - "calendar_date": iso_list, - "time_scale": "UTC", - } - ) - -# ------------- -# READ IN DATA -# ------------- - -KM_TO_M = 1000.0 -M_TO_KM = 1.0 / 1000.0 - -sat_number = "03" -ddm_num = 1 -folder = os.path.join( - os.path.dirname(os.path.abspath(__file__)), "cygnss_data", "" -) -ext = ".npy" - -cyg_file = folder + "cygnss_spacecraft_trajectory.csv" -cyg_pos = data = np.loadtxt( - cyg_file, - delimiter=",", - skiprows=1, - usecols=(1, 2, 3) -)*M_TO_KM -cyg_vel = data = np.loadtxt( - cyg_file, - delimiter=",", - skiprows=1, - usecols=(4, 5, 6) -)*M_TO_KM - -ddm_timestamp_utc = np.loadtxt( - cyg_file, - delimiter=",", - skiprows=1, - usecols=(0,), - dtype="datetime64[ns]" -) - -id = "44" -norad_id_gps = 26407 -norad_id = 41891 -gps_file = folder + "prn_" + id + "_trajectory.csv" -gps_pos = np.genfromtxt( - gps_file, - delimiter=",", - skip_header=1, - usecols=(1, 2, 3), - dtype=float, - filling_values=np.nan -)*M_TO_KM -gps_vel = np.genfromtxt( - gps_file, - delimiter=",", - skip_header=1, - usecols=(4, 5, 6), - dtype=float, - filling_values=np.nan -)*M_TO_KM - -# ----------------------------------- -# Process input into orbitpy objects -# ----------------------------------- - -# Create AbsoluteDateArray from ddm timestamps -datearray = convert_datetime(ddm_timestamp_utc) - -# Create Stateseries from CYGNSS positions/velocities -# sc_stateseries = StateSeries( -# time=datearray, data=[gps_pos, gps_vel], frame=ReferenceFrame.get("ITRF") -# ) -sc_stateseries = StateSeries( - time=datearray, data=[cyg_pos, cyg_vel], frame=ReferenceFrame.get("ITRF") -) - - -# Get first available state for receiver -first_idx = get_first_idx(sc_stateseries.data[0]) -first_pos = sc_stateseries.data[0][first_idx] -first_vel = sc_stateseries.data[1][first_idx] -first_state = np.hstack((first_pos, first_vel)) -start_time = datearray[first_idx] -stop_time = datearray[-1] -duration_days = (stop_time.to_spice_ephemeris_time() - start_time.to_spice_ephemeris_time())/86400.0 -first_state = CartesianState.from_array(first_state, frame=ReferenceFrame.get("ITRF"), time=datearray[first_idx]) - -registry = FrameGraph() -to_frame = ReferenceFrame.get("ICRF_EC") -first_state_icrf = registry.transform(first_state, to_frame, start_time) -sc_stateseries_icrf = registry.transform_series(sc_stateseries, to_frame) - -specs = { - "propagator_type": "OREKIT_PROPAGATOR", - "stepSize": 10, -} - -# duration_days = 1 # Propagate for 1 day -prop = PropagatorFactory.from_dict(specs) -prop.set_drag_coeff(0.0) - -sat_state_series = prop.execute( - t0=start_time, duration_days=duration_days, initial_state=first_state_icrf -) - -diff = sat_state_series - sc_stateseries_icrf - - -## Try again using SGP4 instead. -file_dir = os.path.dirname(__file__) -api = SpaceTrackAPI(os.path.join(file_dir, "spacetrack/credentials.json")) -api.login() - -# Convert your AbsoluteDate to ISO 8601 string format -# If your AbsoluteDate object is 'my_date': -date_str = start_time.to_dict(time_format="Gregorian_Date")["calendar_date"] -omm_dict = api.get_closest_omm( - norad_id=norad_id, # NORAD catalog ID of your satellite - target_date_time=date_str, - within_days=1 # Optional: max days before target to search -) - -# Extract the TLE from the OMM -if omm_dict: - omm = OrbitalMeanElementsMessage.from_dict(omm_dict) - -# Difference in seconds between spacetrack start time and CYGNSS data start time -time_diff = 8000 #abs(omm.epoch.to_spice_ephemeris_time() - start_time.to_spice_ephemeris_time()) -time_diff_days = time_diff / 86400.0 -sat_state_series_sgp4 = prop.execute( - t0=start_time, duration_days=duration_days+time_diff_days, initial_state=omm -) - -diff_st = sat_state_series_sgp4 - sc_stateseries_icrf - -# -------------------------- -# Plot results -# -------------------------- - -# Results for Orekit propagator initialized with state from CYGNSS data - -# Plot individual error components -plt.figure() -plt.plot(sat_state_series.time.ephemeris_time*(1/86400.0), diff.data[0][:, 0], label="Propagated X") -plt.plot(sat_state_series.time.ephemeris_time*(1/86400.0), diff.data[0][:, 1], label="Propagated Y") -plt.plot(sat_state_series.time.ephemeris_time*(1/86400.0), diff.data[0][:, 2], label="Propagated Z") -plt.xlabel("Ephemeris Time (s)") -plt.ylabel("Position (km)") -plt.title("Propagated Satellite Position Over Time") -plt.legend() - -# Plot error norm -plt.figure() -error_norm = np.linalg.norm(diff.data[0], axis=1) -plt.plot(sat_state_series.time.ephemeris_time*(1/86400.0), error_norm) -plt.xlabel("Ephemeris Time (s)") -plt.ylabel("Position Error Norm (km)") -plt.title("Norm of Position Error Between Propagated State and Reference State") - -# Results for SGP4 propagator initialized with TLE from SpaceTrack - -# Plot individual error components -plt.figure() -plt.plot(sat_state_series_sgp4.time.ephemeris_time*(1/86400.0), diff_st.data[0][:, 0], label="Propagated X") -plt.plot(sat_state_series_sgp4.time.ephemeris_time*(1/86400.0), diff_st.data[0][:, 1], label="Propagated Y") -plt.plot(sat_state_series_sgp4.time.ephemeris_time*(1/86400.0), diff_st.data[0][:, 2], label="Propagated Z") -plt.xlabel("Ephemeris Time (s)") -plt.ylabel("Position (km)") -plt.title("Propagated Satellite Position Over Time") -plt.legend() - -# Plot error norm -plt.figure() -error_norm = np.linalg.norm(diff_st.data[0], axis=1) -plt.plot(sat_state_series_sgp4.time.ephemeris_time*(1/86400.0), error_norm) -plt.xlabel("Ephemeris Time (s)") -plt.ylabel("Position Error Norm (km)") -plt.title("Norm of Position Error Between Propagated State and Reference State") -plt.show() \ No newline at end of file diff --git a/examples/specular_validation_prop.py b/examples/specular_validation_prop.py deleted file mode 100644 index 8fc2b67..0000000 --- a/examples/specular_validation_prop.py +++ /dev/null @@ -1,199 +0,0 @@ -""" -Example script demonstrating specular point computation and switching algorithm, which -can select the top (or top-k) specular point trajectories based on the range-corrected -gain factor. The trajectory of rank "k" corresponds to the specular point trajectory with -the k-th highest RCG factor at each time point, using data from any number of GPS trasmitters. - -The simulation uses saved data for CYGNSS position and GPS transmitter positions, and computes the -specular point generated by each GPS/CYGNSS pair. - -Prints the percentage of time points where all four of the top 4 GPS satellites selected by the switching -algorithm match the top 4 GPS satellites from the reference data. - -Then, at each time point, selects the specular point with the lowest error norm vs the reference -specular point trajectory, and plots the error norm over time. - -Next, compares the trajectory of the specular point on the fourth channel from the CYGNSS data to -the trajectory of the specular point generated by the corresponding GPS satellite, chooosing -from among the top-four trajectories. The error norm vs the reference specular point trajectory -is plotted over time. -""" - -import numpy as np -import os -import matplotlib.pyplot as plt - -from eosimutils.time import AbsoluteDateArray -from eosimutils.trajectory import StateSeries, PositionSeries -from eosimutils.base import ReferenceFrame, SurfaceType -from eosimutils.state import CartesianState - -from orbitpy.specular import get_specular_trajectory, get_topk_trajectories -from orbitpy.propagator import PropagatorFactory -from orbitpy.orbits import TwoLineElementSet - -def get_first_idx(arr): - """Get index of first non-naan row in numpy array""" - - mask = ~np.isnan(arr).all(axis=1) - first_idx = np.argmax(mask) - return first_idx - -def convert_datetime(np_dt): - """Convert numpy datetime object to AbsoluteDateArray - - Args: - np_dt (np.ndarray): Numpy datetime64 array. - - Returns: - AbsoluteDateArray: Orbitpy AbsoluteDateArray object with the times from the datetime object - """ - - arr = np.atleast_1d(np_dt) - - if not np.issubdtype(arr.dtype, np.datetime64): - raise TypeError("np_dt must be a numpy.datetime64 scalar or array") - - # Convert to millisecond precision ISO strings without timezone - arr_ms = arr.astype("datetime64[ms]") - iso = np.datetime_as_string(arr_ms, unit="ms", timezone="naive") - iso_list = [str(iso)] if np.isscalar(iso) else iso.astype(str).tolist() - - return AbsoluteDateArray.from_dict( - { - "time_format": "GREGORIAN_DATE", - "calendar_date": iso_list, - "time_scale": "UTC", - } - ) - -# ------------- -# READ IN DATA -# ------------- - -KM_TO_M = 1000.0 -M_TO_KM = 1.0 / 1000.0 - -sat_number = "03" -ddm_num = 1 -folder = os.path.join( - os.path.dirname(os.path.abspath(__file__)), "cygnss_data", "" -) -ext = ".npy" - -cyg_file = folder + "cygnss_spacecraft_trajectory.csv" -cyg_pos = data = np.loadtxt( - cyg_file, - delimiter=",", - skiprows=1, - usecols=(1, 2, 3) -)*M_TO_KM -cyg_vel = data = np.loadtxt( - cyg_file, - delimiter=",", - skiprows=1, - usecols=(4, 5, 6) -)*M_TO_KM - -specular_file = folder + "specular_point_positions_ddm3.csv" -sp_pos = np.loadtxt( - specular_file, - delimiter=",", - skiprows=1, - usecols=(1, 2, 3) -)*M_TO_KM - -ddm_timestamp_utc = np.loadtxt( - cyg_file, - delimiter=",", - skiprows=1, - usecols=(0,), - dtype="datetime64[ns]" -) - -gps_ids = ["43", "44"] -output_list = [] -tx_list = [] -cyg_list = [] -tx_starters = [] -cyg_starters = [] - -for id in gps_ids: - print(f"Processing GPS satellite PRN {id}...") - - gps_file = folder + "prn_" + id + "_trajectory.csv" - gps_pos = np.genfromtxt( - gps_file, - delimiter=",", - skip_header=1, - usecols=(1, 2, 3), - dtype=float, - filling_values=np.nan - )*M_TO_KM - gps_vel = np.genfromtxt( - gps_file, - delimiter=",", - skip_header=1, - usecols=(4, 5, 6), - dtype=float, - filling_values=np.nan - )*M_TO_KM - - - # ----------------------------------- - # Process input into orbitpy objects - # ----------------------------------- - - # Create AbsoluteDateArray from ddm timestamps - datearray = convert_datetime(ddm_timestamp_utc) - - # Create Stateseries from CYGNSS positions/velocities - sc_stateseries = StateSeries( - time=datearray, data=[cyg_pos, cyg_vel], frame=ReferenceFrame.get("ITRF") - ) - - cyg_list.append(sc_stateseries) - - # Create Stateseries from GPS positions/velocities - tx_stateseries = StateSeries( - time=datearray, data=[gps_pos, gps_vel], frame=ReferenceFrame.get("ITRF") - ) - - # Get first available state for transmitter - tx_list.append(tx_stateseries) - first_idx = get_first_idx(tx_stateseries.data[0]) - first_pos = tx_stateseries.data[0][get_first_idx(first_idx)] - first_vel = tx_stateseries.data[1][get_first_idx(first_idx)] - first_state = np.hstack((first_pos, first_vel)) - first_state = CartesianState.from_array(first_state, frame=ReferenceFrame.get("ITRF"), time=datearray[first_idx]) - tx_starters.append(first_state) - - # Get first available state for receiver (should always start at zero) - first_idx = get_first_idx(sc_stateseries.data[0]) - first_pos = sc_stateseries.data[0][get_first_idx(first_idx)] - first_vel = sc_stateseries.data[1][get_first_idx(first_idx)] - first_state = np.hstack((first_pos, first_vel)) - first_state = CartesianState.from_array(first_state, frame=ReferenceFrame.get("ITRF"), time=datearray[first_idx]) - cyg_starters.append(first_state) - - # Create PositionSeries from specular point positions - sp_posseries_ref = PositionSeries( - time=datearray, data=sp_pos, frame=ReferenceFrame.get("ITRF") - ) - - # Compute specular points using ellipsoidal Earth model - sp_posseries_ellipsoidal, rcg_factor_ellipsoidal = get_specular_trajectory( - transmitter_states_itrf=tx_stateseries, - receiver_states_itrf=sc_stateseries, - times=datearray, - surface=SurfaceType.WGS84, - ) - - output_list.append((sp_posseries_ellipsoidal, rcg_factor_ellipsoidal)) - - -plt.figure() -plt.plot(datearray.ephemeris_time, tx_stateseries.data[0][:, 0], label="Reference Specular Point X") -plt.xlabel("Ephemeris Time (s)") -plt.ylabel("va") -plt.show() \ No newline at end of file