{ "cells": [ { "cell_type": "markdown", "id": "187c6bf7", "metadata": {}, "source": [ "# MRO - Orbit Determination Using TNF Doppler Observations\n", "\n", "Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license.\n", "\n", "## Overview\n", "\n", "This notebook performs orbit determination (OD) for the Mars Reconnaissance Orbiter (MRO) using real Doppler tracking data from TNF files. The analysis processes multiple orbital arcs independently, estimating spacecraft state, dynamical model parameters, and empirical accelerations.\n", "\n", "**Key Features:**\n", "- **Multi-arc orbit determination**: Each arc spans approximately 3 days and is processed independently\n", "- **Comprehensive force modeling**: Includes gravity field (120x120), atmospheric drag, solar radiation pressure, empirical accelerations, and third-body perturbations\n", "- **Real observation data**: Uses compressed Doppler measurements from NASA's Deep Space Network (DSN)\n", "- **Parameter estimation**: Estimates initial state, radiation pressure scaling, aerodynamic coefficients, and arc-wise empirical accelerations\n", "- **Parallel processing**: Processes multiple arcs simultaneously using multiprocessing\n", "\n", "**Analysis Workflow:**\n", "1. Load SPICE kernels and TNF observation files for each arc\n", "2. Set up dynamical environment with high-fidelity models\n", "3. Process and filter Doppler observations\n", "4. Define parameters to estimate (state, SRP scaling, drag/lift coefficients, empirical accelerations)\n", "5. Perform orbit determination using weighted least squares estimation\n", "6. Analyze prefit and postfit residuals and state differences from SPICE ephemeris" ] }, { "cell_type": "code", "execution_count": 1, "id": "535efd2c", "metadata": {}, "outputs": [], "source": [ "# Load required standard modules\n", "import os\n", "import pickle\n", "import pandas as pd\n", "import numpy as np\n", "from datetime import datetime, timedelta\n", "import multiprocessing\n", "from matplotlib import pyplot as plt\n", "import time as t" ] }, { "cell_type": "code", "execution_count": 2, "id": "49ef283b", "metadata": {}, "outputs": [], "source": [ "# Load required tudatpy modules\n", "from tudatpy.util import redirect_std\n", "from tudatpy.interface import spice\n", "from tudatpy.astro import time_representation, element_conversion\n", "from tudatpy.data import processTrk234\n", "\n", "from tudatpy.dynamics import (\n", " environment_setup,\n", " propagation_setup,\n", " parameters_setup,\n", " propagation,\n", " parameters,\n", ")\n", "from tudatpy import estimation\n", "from tudatpy.estimation import (\n", " estimation_analysis,\n", " observable_models_setup,\n", " observations,\n", " observations_setup,\n", ")\n", "\n", "from tudatpy.math import interpolators\n", "\n", "# Import custom utility functions\n", "from mro_utils import get_mro_files, macromodel_mro, get_rsw_state_difference" ] }, { "cell_type": "markdown", "id": "d8b03f39", "metadata": {}, "source": [ "## Helper Function: Arc Processing\n", "\n", "This function performs the complete orbit determination workflow for a single arc. It is designed to be called in parallel for multiple arcs.\n", "\n", "**Input Arguments:**\n", "\n", "The `inputs` variable is a list with eleven entries:\n", "1. Arc index (integer identifier for this arc)\n", "2. Start datetime of the arc\n", "3. End datetime of the arc\n", "4. List of TNF files to load\n", "5. List of clock files to load\n", "6. List of orientation kernels to load\n", "7. List of tropospheric correction files to load\n", "8. List of ionospheric correction files to load\n", "9. List of MRO trajectory files to load\n", "10. MRO reference frames definition file to load\n", "11. MRO structure file to load\n", "\n", "**Processing Steps:**\n", "\n", "1. **Setup and File Loading**: Load all SPICE kernels, create output directories, and set up logging\n", "2. **Observation Processing**: Load TNF data, filter to arc time bounds, compress Doppler observations, and compute initial residuals\n", "3. **Environment Setup**: Create high-fidelity dynamical environment with detailed force models\n", "4. **Antenna Reference Point**: Account for antenna position offset from center-of-mass\n", "5. **Observation Model Configuration**: Define light-time corrections and observation simulators\n", "6. **Propagation Settings**: Set up numerical integration and acceleration models\n", "7. **Parameter Estimation Setup**: Define parameters to estimate including state, SRP scaling, drag/lift coefficients, and empirical accelerations\n", "8. **Orbit Determination**: Perform weighted least squares estimation with a priori constraints\n", "9. **Results Analysis**: Save residuals, state differences, and estimation outputs\n", "\n", "**Outputs:**\n", "\n", "For each arc, the function saves:\n", "- `fitLog.txt`: Detailed log of the estimation process\n", "- `estimationOutputDict.pkl`: Estimated parameters, formal errors, and nominal values\n", "- `residDf.pkl`: DataFrame containing prefit, postfit, and SPICE residuals\n", "- `rsw_state_difference_prefit.pkl`: State differences from SPICE before estimation (in RSW frame)\n", "- `rsw_state_difference_postfit.pkl`: State differences from SPICE after estimation (in RSW frame)\n", "- `arc_start_times.pkl`: Times defining empirical acceleration arc boundaries" ] }, { "cell_type": "code", "execution_count": 3, "id": "bbf24a8c", "metadata": {}, "outputs": [], "source": [ "def process_arc(inputs):\n", " \"\"\"Process a single arc and save results\"\"\"\n", "\n", " # Unpack various input arguments\n", " arc_index = inputs[0]\n", "\n", " # Convert start and end datetime objects to Tudat Time variables. A time buffer of one day is subtracted/added to the start/end date\n", " # to ensure that the simulation environment covers the full time span of the loaded TNF files. This is mostly needed because some TNF\n", " # files - while typically assigned to a certain date - actually spans over (slightly) longer than one day. Without this time buffer,\n", " # some observation epochs might thus lie outside the time boundaries within which the dynamical environment is defined.\n", " startDateTime = inputs[1]\n", " endDateTime = inputs[2]\n", "\n", " # Retrieve lists of relevant kernels and input files to load (TNF files, clock and orientation kernels,\n", " # tropospheric and ionospheric corrections)\n", " tnf_files = inputs[3]\n", " clock_files = inputs[4]\n", " orientation_files = inputs[5]\n", " tro_files = inputs[6]\n", " ion_files = inputs[7]\n", " trajectory_files = inputs[8]\n", " frames_def_file = inputs[9]\n", " structure_file = inputs[10]\n", "\n", " # Create output folder for this specific arc\n", " arc_output_folder = f\"{output_folder_base}/arc_{arc_index}/\"\n", " if not os.path.exists(arc_output_folder):\n", " os.makedirs(arc_output_folder)\n", "\n", " # Create log file for this arc\n", " arc_log_file = arc_output_folder + f\"fitLog.txt\"\n", " with open(arc_log_file, \"w\") as f:\n", " f.write(f\"Arc: {arc_index}\\n\")\n", "\n", " print(f\"Processing arc {arc_index}: {startDateTime} to {endDateTime}\")\n", "\n", " ### ------------------------------------------------------------------------------------------\n", " ### LOAD ALL REQUESTED KERNELS AND FILES\n", " ### ------------------------------------------------------------------------------------------\n", "\n", " spice.load_standard_kernels()\n", "\n", " # Load MRO orientation kernels (over the entire relevant time period).\n", " for orientation_file in orientation_files:\n", " spice.load_kernel(orientation_file)\n", "\n", " # Load MRO clock files\n", " for clock_file in clock_files:\n", " spice.load_kernel(clock_file)\n", "\n", " # Load MRO frame definition file (useful for HGA and spacecraft-fixed frames definition)\n", " spice.load_kernel(frames_def_file)\n", "\n", " # Load MRO trajectory kernels\n", " for trajectory_file in trajectory_files:\n", " spice.load_kernel(trajectory_file)\n", "\n", " # Load MRO spacecraft structure file (for antenna position in spacecraft-fixed frame)\n", " spice.load_kernel(structure_file)\n", "\n", " ### ------------------------------------------------------------------------------------------\n", " ### LOAD TNF OBSERVATIONS AND PERFORM PRE-PROCESSING STEPS\n", " ### ------------------------------------------------------------------------------------------\n", "\n", " # Remove first TNF file to avoid issues with time coverage at arc boundaries\n", " tnf_files = tnf_files[1:]\n", "\n", " # Special handling for arc 4: data is in the previous day TNF file\n", " if arc_index == 4:\n", " tnf_files.append(\"mro_kernels/mromagr2012_016_0520xmmmv1.tnf\")\n", "\n", " # Load TNF observations, retaining only Doppler measurements\n", " tnfProcessor = processTrk234.Trk234Processor(\n", " tnf_files,\n", " [\"doppler\"],\n", " spacecraft_name=\"MRO\",\n", " )\n", " original_observations = tnfProcessor.process()\n", "\n", " # Convert arc start/end times from UTC to TDB time scale (used internally by Tudat)\n", " arcStart = time_representation.DateTime.from_python_datetime(\n", " startDateTime\n", " ).to_epoch()\n", " arcEnd = time_representation.DateTime.from_python_datetime(endDateTime).to_epoch()\n", "\n", " time_scale_converter = time_representation.default_time_scale_converter()\n", " arcStart = time_scale_converter.convert_time_object(\n", " input_scale=time_representation.utc_scale,\n", " output_scale=time_representation.tdb_scale,\n", " input_value=time_representation.Time(arcStart),\n", " )\n", " arcEnd = time_scale_converter.convert_time_object(\n", " input_scale=time_representation.utc_scale,\n", " output_scale=time_representation.tdb_scale,\n", " input_value=time_representation.Time(arcEnd),\n", " )\n", "\n", " # Filter observations to retain only those within the arc time interval\n", " arc_filter = observations.observations_processing.observation_filter(\n", " observations.observations_processing.ObservationFilterType.time_bounds_filtering,\n", " arcStart.to_float(),\n", " arcEnd.to_float(),\n", " use_opposite_condition=True,\n", " )\n", " original_observations.filter_observations(arc_filter)\n", " original_observations.remove_empty_observation_sets()\n", "\n", " # Compress Doppler observations from 1.0 s integration time to 60.0 s\n", " # This reduces the number of observations while maintaining data quality\n", " compressed_observations = (\n", " observations_setup.observations_wrapper.create_compressed_doppler_collection(\n", " original_observations, 60, 10\n", " )\n", " )\n", "\n", " # Add transponder delay (time delay in spacecraft electronics)\n", " compressed_observations.set_transponder_delay(\"MRO\", 1.4149e-6)\n", "\n", " # Determine observation and propagation time limits\n", " # Add 1-hour buffer to propagation times to ensure coverage of all observations\n", " observation_time_limits = original_observations.time_bounds_time_object\n", " obs_start_time = observation_time_limits[0]\n", " obs_end_time = observation_time_limits[1]\n", "\n", " prop_start_time = observation_time_limits[0] - 3600.0\n", " prop_end_time = observation_time_limits[1] + 3600.0\n", "\n", " ### ------------------------------------------------------------------------------------------\n", " ### CREATE DYNAMICAL ENVIRONMENT\n", " ### ------------------------------------------------------------------------------------------\n", "\n", " # Create default body settings for celestial bodies\n", " # These bodies will be used for gravitational perturbations\n", " bodies_to_create = [\n", " \"Earth\",\n", " \"Sun\",\n", " \"Mercury\",\n", " \"Venus\",\n", " \"Mars\",\n", " \"Jupiter\",\n", " \"Saturn\",\n", " \"Phobos\",\n", " \"Deimos\",\n", " ]\n", " global_frame_origin = \"SSB\"\n", " global_frame_orientation = \"J2000\"\n", " body_settings = environment_setup.get_default_body_settings_time_limited(\n", " bodies_to_create,\n", " prop_start_time.to_float(),\n", " prop_end_time.to_float(),\n", " global_frame_origin,\n", " global_frame_orientation,\n", " )\n", "\n", " # ====================\n", " # Earth Configuration\n", " # Modify default shape, rotation, and gravity field settings for the Earth\n", " body_settings.get(\"Earth\").shape_settings = (\n", " environment_setup.shape.oblate_spherical_spice()\n", " )\n", " body_settings.get(\"Earth\").rotation_model_settings = (\n", " environment_setup.rotation_model.gcrs_to_itrs(\n", " environment_setup.rotation_model.iau_2006,\n", " global_frame_orientation,\n", " interpolators.interpolator_generation_settings(\n", " interpolators.cubic_spline_interpolation(),\n", " prop_start_time.to_float(),\n", " prop_end_time.to_float(),\n", " 3600.0,\n", " ),\n", " interpolators.interpolator_generation_settings(\n", " interpolators.cubic_spline_interpolation(),\n", " prop_start_time.to_float(),\n", " prop_end_time.to_float(),\n", " 3600.0,\n", " ),\n", " interpolators.interpolator_generation_settings(\n", " interpolators.cubic_spline_interpolation(),\n", " prop_start_time.to_float(),\n", " prop_end_time.to_float(),\n", " 60.0,\n", " ),\n", " )\n", " )\n", " body_settings.get(\"Earth\").gravity_field_settings.associated_reference_frame = (\n", " \"ITRS\"\n", " )\n", "\n", " # Set up DSN ground stations\n", " body_settings.get(\"Earth\").ground_station_settings = (\n", " environment_setup.ground_station.dsn_stations()\n", " )\n", "\n", " # ====================\n", " # Mars Configuration\n", " # Use high-accuracy rotation model and spherical harmonic gravity field (120x120)\n", " body_settings.get(\"Mars\").rotation_model_settings = (\n", " environment_setup.rotation_model.mars_high_accuracy(\n", " base_frame=global_frame_orientation\n", " )\n", " )\n", " body_settings.get(\"Mars\").gravity_field_settings = (\n", " environment_setup.gravity_field.predefined_spherical_harmonic(\n", " environment_setup.gravity_field.jgmro120d, 120\n", " )\n", " )\n", " body_settings.get(\"Mars\").gravity_field_settings.associated_reference_frame = (\n", " \"Mars_Fixed\"\n", " )\n", "\n", " # Define gravity field variations for the tides on Mars\n", " # Models tidal effects from Sun and Phobos using Love number k2 = 0.1697\n", " body_settings.get(\"Mars\").gravity_field_variation_settings = [\n", " environment_setup.gravity_field_variation.solid_body_tide(\"Sun\", 0.1697, 2),\n", " environment_setup.gravity_field_variation.solid_body_tide(\"Phobos\", 0.1697, 2),\n", " ]\n", "\n", " # Define Mars atmosphere settings using Mars-GRAM 2001\n", " body_settings.get(\"Mars\").atmosphere_settings = (\n", " environment_setup.atmosphere.mars_dtm()\n", " )\n", "\n", " # Define Mars irradiance-based radiation pressure settings\n", " # Mars albedo and thermal emission contribute to radiation pressure on MRO\n", " luminosity_settings = (\n", " environment_setup.radiation_pressure.irradiance_based_constant_luminosity(\n", " 250, 3.4e6\n", " )\n", " )\n", " body_settings.get(\"Mars\").radiation_source_settings = (\n", " environment_setup.radiation_pressure.isotropic_radiation_source(\n", " luminosity_settings\n", " )\n", " )\n", "\n", " # ====================\n", " # MRO Spacecraft Configuration\n", " spacecraft_name = \"MRO\"\n", " spacecraft_central_body = \"Mars\"\n", " body_settings.add_empty_settings(spacecraft_name)\n", "\n", " # Retrieve translational ephemeris from SPICE (used as a priori for orbit determination)\n", " body_settings.get(spacecraft_name).ephemeris_settings = (\n", " environment_setup.ephemeris.interpolated_spice(\n", " prop_start_time.to_float(),\n", " prop_end_time.to_float(),\n", " 10.0,\n", " spacecraft_central_body,\n", " global_frame_orientation,\n", " )\n", " )\n", "\n", " # Retrieve rotational ephemeris from SPICE\n", " body_settings.get(spacecraft_name).rotation_model_settings = (\n", " environment_setup.rotation_model.spice(\n", " global_frame_orientation, spacecraft_name + \"_SPACECRAFT\", \"\"\n", " )\n", " )\n", "\n", " # Set spacecraft mass (constant, from MRO mission specifications)\n", " body_settings.get(spacecraft_name).constant_mass = 1262.39 # [kg]\n", " \n", " # Set spacecraft shape using macromodel (detailed surface panel model)\n", " body_settings.get(spacecraft_name).vehicle_shape_settings = macromodel_mro()\n", "\n", " # Create environment\n", " bodies = environment_setup.create_system_of_bodies(body_settings)\n", "\n", " # Set MRO aerodynamics settings using spacecraft macromodel\n", " # Variable cross-section accounts for attitude-dependent drag area\n", " drag_coefficient = 2.0 # from Mazarico et al.\n", " lift_coefficient = 0.01 # small non-zero value to allow estimation\n", " ssh = 0 # surface-to-surface shadowing calculations disabled for speed\n", " aero_coefficient_settings = (\n", " environment_setup.aerodynamic_coefficients.constant_variable_cross_section(\n", " [drag_coefficient, 0, lift_coefficient], ssh\n", " )\n", " )\n", " environment_setup.add_aerodynamic_coefficient_interface(\n", " bodies, spacecraft_name, aero_coefficient_settings\n", " )\n", "\n", " # Set MRO radiation pressure settings using spacecraft macromodel\n", " # Paneled model accounts for detailed surface geometry and optical properties\n", " ssh = 100 # surface-to-surface shadowing grid resolution\n", " occulting_bodies_dict = dict(Sun=[\"Mars\"])\n", " pixel_source_dict = dict(Sun=ssh)\n", " radiation_pressure_settings = (\n", " environment_setup.radiation_pressure.panelled_radiation_target(\n", " occulting_bodies_dict, pixel_source_dict\n", " )\n", " )\n", " environment_setup.add_radiation_pressure_target_model(\n", " bodies, spacecraft_name, radiation_pressure_settings\n", " )\n", "\n", " # Set TNF information (ground station characteristics, media corrections) in bodies\n", " tnfProcessor.set_tnf_information_in_bodies(bodies)\n", "\n", " ### ------------------------------------------------------------------------------------------\n", " ### SET ANTENNA AS REFERENCE POINT FOR DOPPLER OBSERVATIONS\n", " ### ------------------------------------------------------------------------------------------\n", "\n", " # Define MRO center-of-mass (COM) position w.r.t. the origin of the MRO-fixed reference frame\n", " # This accounts for the offset between the spacecraft reference point and COM\n", " com_position = [-0.001235, -1.14978, -0.001288]\n", " antenna_position_history = dict()\n", "\n", " # Create tabulated history of antenna position relative to COM\n", " # This is necessary because Doppler measurements reference the antenna phase center, not COM\n", " for obs_times in compressed_observations.get_observation_times_objects():\n", " time = obs_times[0].to_float() - 3600.0\n", " while time <= obs_times[-1].to_float() + 3600.0:\n", " state = np.zeros((6, 1))\n", "\n", " # For each observation epoch, retrieve the antenna position (spice ID \"-74214\") w.r.t. the origin of the MRO-fixed frame (spice ID \"-74000\")\n", " state[:3, 0] = spice.get_body_cartesian_position_at_epoch(\n", " \"-74214\", \"-74000\", \"MRO_SPACECRAFT\", \"none\", time\n", " )\n", "\n", " # Translate the antenna position to account for the offset between the origin of the MRO-fixed frame and the COM\n", " state[:3, 0] = state[:3, 0] - com_position\n", "\n", " # Store antenna position w.r.t. COM in the MRO-fixed frame\n", " antenna_position_history[time] = state\n", " time += 60.0\n", "\n", " # Create tabulated ephemeris settings from antenna position history\n", " antenna_ephemeris_settings = environment_setup.ephemeris.tabulated(\n", " antenna_position_history, \"-74000\", \"MRO_SPACECRAFT\"\n", " )\n", "\n", " # Create tabulated ephemeris for the MRO antenna\n", " antenna_ephemeris = environment_setup.ephemeris.create_ephemeris(\n", " antenna_ephemeris_settings, \"Antenna\"\n", " )\n", "\n", " # Set the spacecraft's reference point position to that of the antenna (in the MRO-fixed frame)\n", " compressed_observations.set_reference_point(\n", " bodies,\n", " antenna_ephemeris,\n", " \"Antenna\",\n", " \"MRO\",\n", " observable_models_setup.links.LinkEndType.reflector1,\n", " )\n", "\n", " ### ------------------------------------------------------------------------------------------\n", " ### DEFINE OBSERVATION MODEL SETTINGS\n", " ### ------------------------------------------------------------------------------------------\n", "\n", " # Create light-time corrections list\n", " # These corrections account for various effects on signal propagation\n", " light_time_correction_list = list()\n", " \n", " # Add second-order relativistic correction (primarily from Sun's gravitational field)\n", " light_time_correction_list.append(\n", " observable_models_setup.light_time_corrections.approximated_second_order_relativistic_light_time_correction(\n", " [\"Sun\"]\n", " )\n", " )\n", "\n", " # Add tropospheric correction (signal delay through Earth's neutral atmosphere)\n", " light_time_correction_list.append(\n", " observable_models_setup.light_time_corrections.dsn_tabulated_tropospheric_light_time_correction(\n", " tro_files\n", " )\n", " )\n", "\n", " # Add ionospheric correction (signal delay through Earth's ionized atmosphere)\n", " spacecraft_name_per_id = dict()\n", " spacecraft_name_per_id[74] = \"MRO\"\n", " light_time_correction_list.append(\n", " observable_models_setup.light_time_corrections.dsn_tabulated_ionospheric_light_time_correction(\n", " ion_files, spacecraft_name_per_id\n", " )\n", " )\n", "\n", " # Create observation model settings for the Doppler observables\n", " # This defines the link geometry and all applicable corrections\n", " doppler_link_ends = compressed_observations.link_definitions_per_observable[\n", " observable_models_setup.model_settings.dsn_n_way_averaged_doppler_type\n", " ]\n", "\n", " observation_model_settings = list()\n", " for current_link_definition in doppler_link_ends:\n", " observation_model_settings.append(\n", " observable_models_setup.model_settings.dsn_n_way_doppler_averaged(\n", " current_link_definition, light_time_correction_list\n", " )\n", " )\n", "\n", " # Create observation simulators (used to compute predicted observables)\n", " observation_simulators = observations_setup.observations_simulation_settings.create_observation_simulators(\n", " observation_model_settings, bodies\n", " )\n", "\n", " # Compute initial residuals (observations minus predicted values using SPICE ephemeris)\n", " observations.compute_residuals_and_dependent_variables(\n", " compressed_observations, observation_simulators, bodies\n", " )\n", "\n", " # Filter outliers based on residual magnitude\n", " # Observations with residuals > 0.1 Hz are likely corrupted and removed\n", " filter_settings = {\n", " observable_models_setup.model_settings.dsn_n_way_averaged_doppler_type: 0.1,\n", " }\n", "\n", " observation_filters = dict()\n", " for obs_type, threshold in filter_settings.items():\n", " parser = observations.observations_processing.observation_parser(obs_type)\n", " residual_filter = observations.observations_processing.observation_filter(\n", " observations.observations_processing.ObservationFilterType.residual_filtering,\n", " threshold,\n", " )\n", " observation_filters[parser] = residual_filter\n", "\n", " compressed_observations.filter_observations(observation_filters)\n", " linkEndsDict = compressed_observations.link_definition_ids\n", "\n", " # Create DataFrame to store residual information\n", " # This will be populated with SPICE, prefit, and postfit residuals\n", " all_residuals = []\n", " all_times = []\n", " all_type_ids = []\n", " all_link_ends = []\n", "\n", " # Loop through each observable type to get its data\n", " for obs_type, typeName in zip(filter_settings.keys(), [\"doppler\"]):\n", " parser = observations.observations_processing.observation_parser(obs_type)\n", "\n", " # Get residuals, times, and link ends for the current observable type\n", " residuals = compressed_observations.get_concatenated_residuals(parser)\n", " times = compressed_observations.get_concatenated_observation_times(parser)\n", " link_ends_ids = compressed_observations.get_concatenated_link_definition_ids(\n", " parser\n", " )\n", " link_ends = [\n", " linkEndsDict[linkId][\n", " observable_models_setup.links.LinkEndType.transmitter\n", " ].reference_point\n", " + \" - \"\n", " + linkEndsDict[linkId][\n", " observable_models_setup.links.LinkEndType.receiver\n", " ].reference_point\n", " for linkId in link_ends_ids\n", " ]\n", "\n", " # Create a list of type identifiers\n", " type_ids = [typeName] * len(residuals)\n", "\n", " # Append the data to the main lists\n", " all_residuals.extend(residuals)\n", " all_times.extend(times)\n", " all_link_ends.extend(link_ends)\n", " all_type_ids.extend(type_ids)\n", "\n", " # Create DataFrame with all residual data\n", " residDf = pd.DataFrame(\n", " {\n", " \"spice\": all_residuals,\n", " \"time\": all_times,\n", " \"link_ends\": all_link_ends,\n", " \"msrType\": all_type_ids,\n", " }\n", " )\n", "\n", " ### ------------------------------------------------------------------------------------------\n", " ### DEFINE PROPAGATION SETTINGS\n", " ### ------------------------------------------------------------------------------------------\n", "\n", " # Define list of accelerations acting on MRO\n", " # This comprehensive model includes all relevant perturbations\n", " accelerations_settings_spacecraft = dict(\n", " Sun=[\n", " propagation_setup.acceleration.point_mass_gravity(), # Solar gravitational attraction\n", " propagation_setup.acceleration.radiation_pressure( # Solar radiation pressure\n", " environment_setup.radiation_pressure.paneled_target\n", " ),\n", " ],\n", " Mars=[\n", " propagation_setup.acceleration.spherical_harmonic_gravity(120, 120), # Mars gravity field\n", " propagation_setup.acceleration.aerodynamic(), # Atmospheric drag\n", " propagation_setup.acceleration.radiation_pressure( # Mars albedo and thermal emission\n", " environment_setup.radiation_pressure.paneled_target\n", " ),\n", " propagation_setup.acceleration.empirical(), # Empirical accelerations (to be estimated)\n", " ],\n", " Jupiter=[propagation_setup.acceleration.point_mass_gravity()], # Third-body perturbation\n", " Saturn=[propagation_setup.acceleration.point_mass_gravity()], # Third-body perturbation\n", " Earth=[propagation_setup.acceleration.point_mass_gravity()], # Third-body perturbation\n", " Phobos=[propagation_setup.acceleration.point_mass_gravity()], # Third-body perturbation\n", " Deimos=[propagation_setup.acceleration.point_mass_gravity()], # Third-body perturbation\n", " )\n", "\n", " # Create accelerations settings dictionary\n", " acceleration_settings = {spacecraft_name: accelerations_settings_spacecraft}\n", "\n", " # Create acceleration models from settings\n", " bodies_to_propagate = [spacecraft_name]\n", " central_bodies = [spacecraft_central_body]\n", " acceleration_models = propagation_setup.create_acceleration_models(\n", " bodies, acceleration_settings, bodies_to_propagate, central_bodies\n", " )\n", "\n", " # Define integrator settings\n", " # RKF78 is a high-order adaptive integrator suitable for precise orbit propagation\n", " integration_step = 30.0\n", " integrator_settings = propagation_setup.integrator.runge_kutta_fixed_step_size(\n", " time_representation.Time(0, integration_step),\n", " propagation_setup.integrator.rkf_78,\n", " )\n", "\n", " # Retrieve initial state from SPICE (this will be refined during estimation)\n", " initial_state = propagation.get_state_of_bodies(\n", " bodies_to_propagate, central_bodies, bodies, prop_start_time\n", " )\n", "\n", " # Define propagator settings\n", " propagator_settings = propagation_setup.propagator.translational(\n", " central_bodies,\n", " acceleration_models,\n", " bodies_to_propagate,\n", " initial_state,\n", " prop_start_time,\n", " integrator_settings,\n", " propagation_setup.propagator.time_termination(obs_end_time.to_float()),\n", " )\n", "\n", " ### ------------------------------------------------------------------------------------------\n", " ### DEFINE SET OF PARAMETERS TO BE ESTIMATED\n", " ### ------------------------------------------------------------------------------------------\n", "\n", " # Define parameters to estimate\n", " # Start with the initial state (position and velocity at arc start)\n", " parameter_settings = parameters_setup.initial_states(propagator_settings, bodies)\n", "\n", " # Define list of additional parameters beyond initial state\n", " extra_parameters = []\n", "\n", " # Solar radiation pressure scaling parameter\n", " # Accounts for uncertainties in surface optical properties and solar flux\n", " extra_parameters = [\n", " parameters_setup.radiation_pressure_target_direction_scaling(\n", " spacecraft_name, \"Sun\"\n", " ),\n", " ]\n", " \n", " # Define arc start times for arc-wise empirical accelerations\n", " # Empirical accelerations are estimated separately for each orbital revolution\n", " # This accounts for unmodeled forces (e.g., imperfect gravity field, attitude errors)\n", " mars_gravitational_parameter = bodies.get(\"Mars\").gravitational_parameter\n", " keplerian_state = element_conversion.cartesian_to_keplerian(\n", " initial_state, mars_gravitational_parameter\n", " )\n", " semi_major_axis = keplerian_state[0]\n", " orbital_period = (\n", " 2.0 * np.pi * np.sqrt(semi_major_axis**3 / mars_gravitational_parameter)\n", " )\n", "\n", " # Create list of arc start times (one per orbit)\n", " arc_start_times = []\n", " current_arc_start_time = obs_start_time.to_float()\n", " while current_arc_start_time < obs_end_time.to_float():\n", " arc_start_times.append(current_arc_start_time)\n", " current_arc_start_time += orbital_period\n", "\n", " # Save arc start times for later analysis\n", " with open(arc_output_folder + f\"arc_start_times.pkl\", \"wb\") as f:\n", " pickle.dump(arc_start_times, f)\n", "\n", " # Define empirical acceleration components to estimate for each arc\n", " # RSW frame: R=radial, S=along-track, W=cross-track\n", " # Each component can have constant, sine, and cosine terms (Fourier series)\n", " acceleration_components_to_estimate = {\n", " parameters_setup.EmpiricalAccelerationComponents.along_track_empirical_acceleration_component: [\n", " parameters_setup.EmpiricalAccelerationFunctionalShapes.constant_empirical,\n", " parameters_setup.EmpiricalAccelerationFunctionalShapes.sine_empirical,\n", " parameters_setup.EmpiricalAccelerationFunctionalShapes.cosine_empirical,\n", " ],\n", " parameters_setup.EmpiricalAccelerationComponents.across_track_empirical_acceleration_component: [\n", " parameters_setup.EmpiricalAccelerationFunctionalShapes.constant_empirical,\n", " parameters_setup.EmpiricalAccelerationFunctionalShapes.sine_empirical,\n", " parameters_setup.EmpiricalAccelerationFunctionalShapes.cosine_empirical,\n", " ],\n", " }\n", " extra_parameters.append(\n", " parameters_setup.arcwise_empirical_accelerations(\n", " spacecraft_name,\n", " \"Mars\",\n", " acceleration_components_to_estimate,\n", " arc_start_times,\n", " )\n", " )\n", "\n", " # Add aerodynamic coefficient scaling parameters\n", " # These account for uncertainties in drag and lift coefficients\n", " extra_parameters.append(\n", " parameters_setup.drag_component_scaling(spacecraft_name),\n", " )\n", " extra_parameters.append(\n", " parameters_setup.lift_component_scaling(spacecraft_name),\n", " )\n", "\n", " # Add additional parameters settings\n", " parameter_settings += extra_parameters\n", "\n", " # Create set of parameters to estimate\n", " parameters_to_estimate = parameters_setup.create_parameter_set(\n", " parameter_settings, bodies, propagator_settings\n", " )\n", "\n", " nominal_parameters = parameters_to_estimate.parameter_vector\n", "\n", " # Define a priori uncertainties for each parameter\n", " # These are used to weight the parameters in the least squares estimation\n", " posSigma = [1e3] * 3 # Position: 1 km\n", " velSigma = [1e-1] * 3 # Velocity: 10 cm/s\n", " srpScaleSigma = [2] # SRP scaling: factor of 2 uncertainty\n", " dragSigma = [2] # Drag coefficient: factor of 2 uncertainty\n", " liftSigma = [2] # Lift coefficient: factor of 2 uncertainty\n", " radialAcc = [] # No radial empirical accelerations\n", " alongAcc = [1e-6] * 3 * len(arc_start_times) # Along-track: 1 µm/s²\n", " acrossAcc = [1e-6] * 3 * len(arc_start_times) # Cross-track: 1 µm/s²\n", " all_sigmas = (\n", " posSigma\n", " + velSigma\n", " + srpScaleSigma\n", " + dragSigma\n", " + liftSigma\n", " + radialAcc\n", " + alongAcc\n", " + acrossAcc\n", " )\n", "\n", " # Create the a priori covariance matrix (diagonal) from the sigmas\n", " # Inverse covariance is used as weight matrix in least squares\n", " apriori_covariance = np.diag(1 / np.square(all_sigmas))\n", "\n", " ### ------------------------------------------------------------------------------------------\n", " ### DEFINE ESTIMATION SETTINGS AND PERFORM THE FIT\n", " ### ------------------------------------------------------------------------------------------\n", "\n", " # Create estimator object\n", " # This sets up the orbit determination problem with all models and settings\n", " estimator = estimation_analysis.Estimator(\n", " bodies, parameters_to_estimate, observation_model_settings, propagator_settings\n", " )\n", "\n", " # Define estimation settings\n", " # Convergence checker stops after 6 iterations or when RMS residual change < threshold\n", " estimation_input = estimation_analysis.EstimationInput(\n", " compressed_observations,\n", " inverse_apriori_covariance=apriori_covariance,\n", " convergence_checker=estimation_analysis.estimation_convergence_checker(6),\n", " )\n", " estimation_input.define_estimation_settings(\n", " reintegrate_equations_on_first_iteration=False, # Use SPICE state as initial guess\n", " reintegrate_variational_equations=True, # Recompute sensitivities each iteration\n", " print_output_to_terminal=True, # Show progress\n", " save_state_history_per_iteration=True, # Save orbit for each iteration\n", " )\n", "\n", " # Perform estimation\n", " # Output is redirected to log file for this arc\n", " with redirect_std(arc_log_file, True, True):\n", " print(f\"Arc {arc_index} interval:\")\n", " print(\n", " time_representation.DateTime.from_epoch(arcStart),\n", " time_representation.DateTime.from_epoch(arcEnd),\n", " )\n", " print(\n", " \"Observation time bounds:\",\n", " time_representation.DateTime.from_epoch(obs_start_time),\n", " \" - \",\n", " time_representation.DateTime.from_epoch(obs_end_time),\n", " )\n", "\n", " print(f\"Propagation time limits:\")\n", " print(\n", " time_representation.DateTime.from_epoch(prop_start_time),\n", " time_representation.DateTime.from_epoch(prop_end_time),\n", " )\n", " parameters.print_parameter_names(parameters_to_estimate)\n", " print(\n", " \"Number of parameters to estimate:\",\n", " len(nominal_parameters),\n", " )\n", " print(\"Nominal values +- apriori:\")\n", " for value, sigma in zip(nominal_parameters, all_sigmas):\n", " print(f\"{value:.6e} +- {sigma:.6e}\")\n", " print(\"\\n\")\n", " estimation_output = estimator.perform_estimation(estimation_input)\n", " print(\"\\n\")\n", " print(\"Corrected values +- uncertainty:\")\n", " for value, sigma in zip(\n", " estimation_output.final_parameters, estimation_output.formal_errors\n", " ):\n", " print(f\"{value:.6e} +- {sigma:.6e}\")\n", "\n", " # Save estimation output dictionary\n", " outputDict = {}\n", " outputDict[\"nominal\"] = nominal_parameters\n", " outputDict[\"correted\"] = estimation_output.final_parameters\n", " outputDict[\"formal_errors\"] = estimation_output.formal_errors\n", "\n", " with open(arc_output_folder + f\"estimationOutputDict.pkl\", \"wb\") as f:\n", " pickle.dump(outputDict, f)\n", "\n", " ### ------------------------------------------------------------------------------------------\n", " ### COLLECT AND SAVE RESULTS\n", " ### ------------------------------------------------------------------------------------------\n", "\n", " # Collect results\n", " bestIterIndex = estimation_output.best_iteration\n", " residDf[\"prefit\"] = estimation_output.residual_history[:, 0]\n", " residDf[\"postfit\"] = estimation_output.residual_history[:, bestIterIndex]\n", " residDf.to_pickle(arc_output_folder + f\"residDf.pkl\")\n", "\n", " # Compute state differences from SPICE ephemeris (prefit)\n", " prefit_state_history = estimation_output.simulation_results_per_iteration[\n", " 0\n", " ].dynamics_results.state_history_float\n", "\n", " rsw_state_difference = get_rsw_state_difference(\n", " prefit_state_history,\n", " spacecraft_name,\n", " spacecraft_central_body,\n", " global_frame_orientation,\n", " )\n", "\n", " rsw_df = pd.DataFrame(\n", " rsw_state_difference, columns=[\"t\", \"R\", \"T\", \"N\", \"vR\", \"vT\", \"vN\"]\n", " )\n", " rsw_df.to_pickle(arc_output_folder + f\"rsw_state_difference_prefit.pkl\")\n", " \n", " # Compute state differences from SPICE ephemeris (postfit)\n", " estimated_state_history = estimation_output.simulation_results_per_iteration[\n", " bestIterIndex\n", " ].dynamics_results.state_history_float\n", "\n", " rsw_state_difference = get_rsw_state_difference(\n", " estimated_state_history,\n", " spacecraft_name,\n", " spacecraft_central_body,\n", " global_frame_orientation,\n", " )\n", "\n", " rsw_df = pd.DataFrame(\n", " rsw_state_difference, columns=[\"t\", \"R\", \"T\", \"N\", \"vR\", \"vT\", \"vN\"]\n", " )\n", " rsw_df.to_pickle(arc_output_folder + f\"rsw_state_difference_postfit.pkl\")\n", "\n", " print(f\"Finished processing arc {arc_index}\")\n", "\n", " return arc_index" ] }, { "cell_type": "markdown", "id": "4a17b576", "metadata": {}, "source": [ "## Main Execution\n", "\n", "### Setup Configuration\n", "\n", "Define the arcs to process and set up the output directory structure." ] }, { "cell_type": "code", "execution_count": 4, "id": "2587a3f8", "metadata": {}, "outputs": [], "source": [ "if __name__ == \"__main__\":\n", " exec_start_time = t.time()\n", "\n", " # Set up the output folder\n", " output_folder_base = \"mro_outputs\"\n", " if not os.path.exists(output_folder_base):\n", " os.makedirs(output_folder_base)\n", "\n", " # Define arcs to process\n", " # Each tuple contains (start_datetime, end_datetime) for one arc\n", " # Arcs are approximately 3 days long and span January 1-22, 2012\n", " arcs = [\n", " (\n", " datetime.fromisoformat(\"2012-01-01 03:18:01.965\"),\n", " datetime.fromisoformat(\"2012-01-04 01:58:15.132\"),\n", " ),\n", " (\n", " datetime.fromisoformat(\"2012-01-04 02:25:09.706\"),\n", " datetime.fromisoformat(\"2012-01-07 02:55:23.122\"),\n", " ),\n", " (\n", " datetime.fromisoformat(\"2012-01-07 03:23:14.407\"),\n", " datetime.fromisoformat(\"2012-01-10 02:03:27.113\"),\n", " ),\n", " (\n", " datetime.fromisoformat(\"2012-01-10 02:22:44.539\"),\n", " datetime.fromisoformat(\"2012-01-13 02:52:58.104\"),\n", " ),\n", " (\n", " datetime.fromisoformat(\"2012-01-13 03:15:38.112\"),\n", " datetime.fromisoformat(\"2012-01-16 02:05:51.095\"),\n", " ),\n", " (\n", " datetime.fromisoformat(\"2012-01-16 02:22:44.352\"),\n", " datetime.fromisoformat(\"2012-01-19 02:52:57.085\"),\n", " ),\n", " (\n", " datetime.fromisoformat(\"2012-01-19 03:17:17.831\"),\n", " datetime.fromisoformat(\"2012-01-22 01:57:31.076\"),\n", " ),\n", " ]\n", "\n", " # Set up multiprocessing pool\n", " num_processes = len(arcs)" ] }, { "cell_type": "markdown", "id": "cf4438d9", "metadata": {}, "source": [ "### Prepare Input Data for Each Arc\n", "\n", "Retrieve all necessary SPICE kernels and data files for each arc. Files are downloaded automatically if not found locally." ] }, { "cell_type": "code", "execution_count": 5, "id": "59bc1cb9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Files for arc 0\n", "---------------------------------------------\n", "Download MRO clock \n", "relevant clock files\n", "mro_kernels/mro_sclkscet_00112_65536.tsc\n", "---------------------------------------------\n", "Download MRO orientation kernels\n", "relevant orientation files\n", "mro_kernels/mro_sc_psp_111227_120102.bc\n", "mro_kernels/mro_sc_psp_120103_120109.bc\n", "mro_kernels/mro_hga_psp_120103_120109.bc\n", "mro_kernels/mro_hga_psp_111227_120102.bc\n", "mro_kernels/mro_sa_psp_120103_120109.bc\n", "mro_kernels/mro_sa_psp_111227_120102.bc\n", "---------------------------------------------\n", "Download MRO tropospheric corrections files\n", "relevant tropospheric corrections files\n", "mro_kernels/mromagr2012_001_2012_032.tro\n", "mro_kernels/mromagr2011_335_2012_001.tro\n", "---------------------------------------------\n", "Download MRO ionospheric corrections files\n", "relevant ionospheric corrections files\n", "mro_kernels/mromagr2012_001_2012_032.ion\n", "mro_kernels/mromagr2011_335_2012_001.ion\n", "---------------------------------------------\n", "Download MRO TNF files\n", "nb existing files 21\n", "relevant TNF files\n", "mro_kernels/mromagr2011_365_1411xmmmv1.tnf\n", "mro_kernels/mromagr2012_001_2220xmmmv1.tnf\n", "mro_kernels/mromagr2012_002_1426xmmmv1.tnf\n", "mro_kernels/mromagr2012_003_1407xmmmv1.tnf\n", "mro_kernels/mromagr2012_004_1550xmmmv1.tnf\n", "---------------------------------------------\n", "Download MRO trajectory files\n", "relevant trajectory files\n", "mro_kernels/mro_psp21.bsp\n", "mro_kernels/mro_psp22.bsp\n", "mro_kernels/mro_psp23.bsp\n", "mro_kernels/mro_psp24.bsp\n", "mro_kernels/mro_psp25.bsp\n", "---------------------------------------------\n", "Download MRO frames definition file\n", "relevant MRO frames definition file\n", "mro_kernels/mro_v16.tf\n", "---------------------------------------------\n", "Download MRO structure file\n", "relevant MRO structure file\n", "mro_kernels/mro_struct_v10.bsp\n", "\n", "\n", "Files for arc 1\n", "---------------------------------------------\n", "Download MRO clock \n", "relevant clock files\n", "mro_kernels/mro_sclkscet_00112_65536.tsc\n", "---------------------------------------------\n", "Download MRO orientation kernels\n", "relevant orientation files\n", "mro_kernels/mro_sc_psp_111227_120102.bc\n", "mro_kernels/mro_sc_psp_120103_120109.bc\n", "mro_kernels/mro_hga_psp_120103_120109.bc\n", "mro_kernels/mro_hga_psp_111227_120102.bc\n", "mro_kernels/mro_sa_psp_120103_120109.bc\n", "mro_kernels/mro_sa_psp_111227_120102.bc\n", "---------------------------------------------\n", "Download MRO tropospheric corrections files\n", "relevant tropospheric corrections files\n", "mro_kernels/mromagr2012_001_2012_032.tro\n", "---------------------------------------------\n", "Download MRO ionospheric corrections files\n", "relevant ionospheric corrections files\n", "mro_kernels/mromagr2012_001_2012_032.ion\n", "---------------------------------------------\n", "Download MRO TNF files\n", "nb existing files 21\n", "relevant TNF files\n", "mro_kernels/mromagr2012_003_1407xmmmv1.tnf\n", "mro_kernels/mromagr2012_004_1550xmmmv1.tnf\n", "mro_kernels/mromagr2012_005_1255xmmmv1.tnf\n", "mro_kernels/mromagr2012_006_1355xmmmv1.tnf\n", "mro_kernels/mromagr2012_007_1640xmmmv1.tnf\n", "mro_kernels/mromagr2012_008_2200xmmmv1.tnf\n", "---------------------------------------------\n", "Download MRO trajectory files\n", "relevant trajectory files\n", "mro_kernels/mro_psp21.bsp\n", "mro_kernels/mro_psp22.bsp\n", "mro_kernels/mro_psp23.bsp\n", "mro_kernels/mro_psp24.bsp\n", "mro_kernels/mro_psp25.bsp\n", "---------------------------------------------\n", "Download MRO frames definition file\n", "relevant MRO frames definition file\n", "mro_kernels/mro_v16.tf\n", "---------------------------------------------\n", "Download MRO structure file\n", "relevant MRO structure file\n", "mro_kernels/mro_struct_v10.bsp\n", "\n", "\n", "Files for arc 2\n", "---------------------------------------------\n", "Download MRO clock \n", "relevant clock files\n", "mro_kernels/mro_sclkscet_00112_65536.tsc\n", "---------------------------------------------\n", "Download MRO orientation kernels\n", "relevant orientation files\n", "mro_kernels/mro_sc_psp_120110_120116.bc\n", "mro_kernels/mro_sc_psp_120103_120109.bc\n", "mro_kernels/mro_hga_psp_120103_120109.bc\n", "mro_kernels/mro_hga_psp_120110_120116.bc\n", "mro_kernels/mro_sa_psp_120110_120116.bc\n", "mro_kernels/mro_sa_psp_120103_120109.bc\n", "---------------------------------------------\n", "Download MRO tropospheric corrections files\n", "relevant tropospheric corrections files\n", "mro_kernels/mromagr2012_001_2012_032.tro\n", "---------------------------------------------\n", "Download MRO ionospheric corrections files\n", "relevant ionospheric corrections files\n", "mro_kernels/mromagr2012_001_2012_032.ion\n", "---------------------------------------------\n", "Download MRO TNF files\n", "nb existing files 21\n", "relevant TNF files\n", "mro_kernels/mromagr2012_006_1355xmmmv1.tnf\n", "mro_kernels/mromagr2012_007_1640xmmmv1.tnf\n", "mro_kernels/mromagr2012_008_2200xmmmv1.tnf\n", "mro_kernels/mromagr2012_009_0545xmmmv1.tnf\n", "mro_kernels/mromagr2012_010_1345xmmmv1.tnf\n", "---------------------------------------------\n", "Download MRO trajectory files\n", "relevant trajectory files\n", "mro_kernels/mro_psp21.bsp\n", "mro_kernels/mro_psp22.bsp\n", "mro_kernels/mro_psp23.bsp\n", "mro_kernels/mro_psp24.bsp\n", "mro_kernels/mro_psp25.bsp\n", "---------------------------------------------\n", "Download MRO frames definition file\n", "relevant MRO frames definition file\n", "mro_kernels/mro_v16.tf\n", "---------------------------------------------\n", "Download MRO structure file\n", "relevant MRO structure file\n", "mro_kernels/mro_struct_v10.bsp\n", "\n", "\n", "Files for arc 3\n", "---------------------------------------------\n", "Download MRO clock \n", "relevant clock files\n", "mro_kernels/mro_sclkscet_00112_65536.tsc\n", "---------------------------------------------\n", "Download MRO orientation kernels\n", "relevant orientation files\n", "mro_kernels/mro_sc_psp_120110_120116.bc\n", "mro_kernels/mro_sc_psp_120103_120109.bc\n", "mro_kernels/mro_hga_psp_120103_120109.bc\n", "mro_kernels/mro_hga_psp_120110_120116.bc\n", "mro_kernels/mro_sa_psp_120110_120116.bc\n", "mro_kernels/mro_sa_psp_120103_120109.bc\n", "---------------------------------------------\n", "Download MRO tropospheric corrections files\n", "relevant tropospheric corrections files\n", "mro_kernels/mromagr2012_001_2012_032.tro\n", "---------------------------------------------\n", "Download MRO ionospheric corrections files\n", "relevant ionospheric corrections files\n", "mro_kernels/mromagr2012_001_2012_032.ion\n", "---------------------------------------------\n", "Download MRO TNF files\n", "nb existing files 21\n", "relevant TNF files\n", "mro_kernels/mromagr2012_009_0545xmmmv1.tnf\n", "mro_kernels/mromagr2012_010_1345xmmmv1.tnf\n", "mro_kernels/mromagr2012_011_1900xmmmv1.tnf\n", "mro_kernels/mromagr2012_012_0620xmmmv1.tnf\n", "mro_kernels/mromagr2012_013_1405xmmmv1.tnf\n", "mro_kernels/mromagr2012_014_2250xmmmv1.tnf\n", "---------------------------------------------\n", "Download MRO trajectory files\n", "relevant trajectory files\n", "mro_kernels/mro_psp21.bsp\n", "mro_kernels/mro_psp22.bsp\n", "mro_kernels/mro_psp23.bsp\n", "mro_kernels/mro_psp24.bsp\n", "mro_kernels/mro_psp25.bsp\n", "---------------------------------------------\n", "Download MRO frames definition file\n", "relevant MRO frames definition file\n", "mro_kernels/mro_v16.tf\n", "---------------------------------------------\n", "Download MRO structure file\n", "relevant MRO structure file\n", "mro_kernels/mro_struct_v10.bsp\n", "\n", "\n", "Files for arc 4\n", "---------------------------------------------\n", "Download MRO clock \n", "relevant clock files\n", "mro_kernels/mro_sclkscet_00112_65536.tsc\n", "---------------------------------------------\n", "Download MRO orientation kernels\n", "relevant orientation files\n", "mro_kernels/mro_sc_psp_120110_120116.bc\n", "mro_kernels/mro_sc_psp_120117_120123.bc\n", "mro_kernels/mro_hga_psp_120117_120123.bc\n", "mro_kernels/mro_hga_psp_120110_120116.bc\n", "mro_kernels/mro_sa_psp_120110_120116.bc\n", "mro_kernels/mro_sa_psp_120117_120123.bc\n", "---------------------------------------------\n", "Download MRO tropospheric corrections files\n", "relevant tropospheric corrections files\n", "mro_kernels/mromagr2012_001_2012_032.tro\n", "---------------------------------------------\n", "Download MRO ionospheric corrections files\n", "relevant ionospheric corrections files\n", "mro_kernels/mromagr2012_001_2012_032.ion\n", "---------------------------------------------\n", "Download MRO TNF files\n", "nb existing files 21\n", "relevant TNF files\n", "mro_kernels/mromagr2012_012_0620xmmmv1.tnf\n", "mro_kernels/mromagr2012_013_1405xmmmv1.tnf\n", "mro_kernels/mromagr2012_014_2250xmmmv1.tnf\n", "mro_kernels/mromagr2012_016_0520xmmmv1.tnf\n", "---------------------------------------------\n", "Download MRO trajectory files\n", "relevant trajectory files\n", "mro_kernels/mro_psp21.bsp\n", "mro_kernels/mro_psp22.bsp\n", "mro_kernels/mro_psp23.bsp\n", "mro_kernels/mro_psp24.bsp\n", "mro_kernels/mro_psp25.bsp\n", "---------------------------------------------\n", "Download MRO frames definition file\n", "relevant MRO frames definition file\n", "mro_kernels/mro_v16.tf\n", "---------------------------------------------\n", "Download MRO structure file\n", "relevant MRO structure file\n", "mro_kernels/mro_struct_v10.bsp\n", "\n", "\n", "Files for arc 5\n", "---------------------------------------------\n", "Download MRO clock \n", "relevant clock files\n", "mro_kernels/mro_sclkscet_00112_65536.tsc\n", "---------------------------------------------\n", "Download MRO orientation kernels\n", "relevant orientation files\n", "mro_kernels/mro_sc_psp_120110_120116.bc\n", "mro_kernels/mro_sc_psp_120117_120123.bc\n", "mro_kernels/mro_hga_psp_120117_120123.bc\n", "mro_kernels/mro_hga_psp_120110_120116.bc\n", "mro_kernels/mro_sa_psp_120110_120116.bc\n", "mro_kernels/mro_sa_psp_120117_120123.bc\n", "---------------------------------------------\n", "Download MRO tropospheric corrections files\n", "relevant tropospheric corrections files\n", "mro_kernels/mromagr2012_001_2012_032.tro\n", "---------------------------------------------\n", "Download MRO ionospheric corrections files\n", "relevant ionospheric corrections files\n", "mro_kernels/mromagr2012_001_2012_032.ion\n", "---------------------------------------------\n", "Download MRO TNF files\n", "nb existing files 21\n", "relevant TNF files\n", "mro_kernels/mromagr2012_016_0520xmmmv1.tnf\n", "mro_kernels/mromagr2012_017_0520xmmmv1.tnf\n", "mro_kernels/mromagr2012_018_2200xmmmv1.tnf\n", "mro_kernels/mromagr2012_019_1230xmmmv1.tnf\n", "mro_kernels/mromagr2012_020_1315xmmmv1.tnf\n", "---------------------------------------------\n", "Download MRO trajectory files\n", "relevant trajectory files\n", "mro_kernels/mro_psp21.bsp\n", "mro_kernels/mro_psp22.bsp\n", "mro_kernels/mro_psp23.bsp\n", "mro_kernels/mro_psp24.bsp\n", "mro_kernels/mro_psp25.bsp\n", "---------------------------------------------\n", "Download MRO frames definition file\n", "relevant MRO frames definition file\n", "mro_kernels/mro_v16.tf\n", "---------------------------------------------\n", "Download MRO structure file\n", "relevant MRO structure file\n", "mro_kernels/mro_struct_v10.bsp\n", "\n", "\n", "Files for arc 6\n", "---------------------------------------------\n", "Download MRO clock \n", "relevant clock files\n", "mro_kernels/mro_sclkscet_00112_65536.tsc\n", "---------------------------------------------\n", "Download MRO orientation kernels\n", "relevant orientation files\n", "mro_kernels/mro_sc_psp_120117_120123.bc\n", "mro_kernels/mro_hga_psp_120117_120123.bc\n", "mro_kernels/mro_sa_psp_120117_120123.bc\n", "---------------------------------------------\n", "Download MRO tropospheric corrections files\n", "relevant tropospheric corrections files\n", "mro_kernels/mromagr2012_001_2012_032.tro\n", "---------------------------------------------\n", "Download MRO ionospheric corrections files\n", "relevant ionospheric corrections files\n", "mro_kernels/mromagr2012_001_2012_032.ion\n", "---------------------------------------------\n", "Download MRO TNF files\n", "nb existing files 21\n", "relevant TNF files\n", "mro_kernels/mromagr2012_018_2200xmmmv1.tnf\n", "mro_kernels/mromagr2012_019_1230xmmmv1.tnf\n", "mro_kernels/mromagr2012_020_1315xmmmv1.tnf\n", "mro_kernels/mromagr2012_022_0505xmmmv1.tnf\n", "---------------------------------------------\n", "Download MRO trajectory files\n", "relevant trajectory files\n", "mro_kernels/mro_psp21.bsp\n", "mro_kernels/mro_psp22.bsp\n", "mro_kernels/mro_psp23.bsp\n", "mro_kernels/mro_psp24.bsp\n", "mro_kernels/mro_psp25.bsp\n", "---------------------------------------------\n", "Download MRO frames definition file\n", "relevant MRO frames definition file\n", "mro_kernels/mro_v16.tf\n", "---------------------------------------------\n", "Download MRO structure file\n", "relevant MRO structure file\n", "mro_kernels/mro_struct_v10.bsp\n", "\n", "\n" ] } ], "source": [ "inputs = []\n", "for i, arc in enumerate(arcs):\n", "\n", " startEpoch = arc[0]\n", " endEpoch = arc[1]\n", " # Add 1-day buffer to ensure file coverage\n", " startEpochWithBuffer = startEpoch - timedelta(days=1)\n", " endEpochWithBuffer = endEpoch + timedelta(days=1)\n", "\n", " print(\"Files for arc {}\".format(i))\n", " # First retrieve the names of all the relevant kernels and data files necessary to cover the specified time interval\n", " (\n", " clock_files,\n", " orientation_files,\n", " tro_files,\n", " ion_files,\n", " tnf_files,\n", " trajectory_files,\n", " frames_def_file,\n", " structure_file,\n", " ) = get_mro_files(\"mro_kernels/\", startEpochWithBuffer, endEpochWithBuffer)\n", " print(\"\\n\")\n", "\n", " # Construct a list of input arguments containing the arguments needed this specific parallel run.\n", " # These include the start and end dates, along with the names of all relevant kernels and data files that should be loaded\n", " inputs.append(\n", " [\n", " i,\n", " startEpoch,\n", " endEpoch,\n", " tnf_files,\n", " clock_files,\n", " orientation_files,\n", " tro_files,\n", " ion_files,\n", " trajectory_files,\n", " frames_def_file,\n", " structure_file,\n", " ]\n", " )" ] }, { "cell_type": "markdown", "id": "85aaafa6", "metadata": {}, "source": [ "### Run Parallel Orbit Determination\n", "\n", "Process all arcs in parallel using multiprocessing. Each arc is processed independently on a separate CPU core.\n", "\n", "**Processing Details:**\n", "- Each arc takes approximately 10-30 minutes to process depending on hardware\n", "- Results are saved automatically to `mro_outputs/arc_X/` directories\n", "- Progress can be monitored through log files: `mro_outputs/arc_X/fitLog.txt`" ] }, { "cell_type": "code", "execution_count": 6, "id": "ed47de82", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing arc 0: 2012-01-01 03:18:01.965000 to 2012-01-04 01:58:15.132000Processing arc 1: 2012-01-04 02:25:09.706000 to 2012-01-07 02:55:23.122000Processing arc 3: 2012-01-10 02:22:44.539000 to 2012-01-13 02:52:58.104000Processing arc 5: 2012-01-16 02:22:44.352000 to 2012-01-19 02:52:57.085000Processing arc 2: 2012-01-07 03:23:14.407000 to 2012-01-10 02:03:27.113000Processing arc 6: 2012-01-19 03:17:17.831000 to 2012-01-22 01:57:31.076000Processing arc 4: 2012-01-13 03:15:38.112000 to 2012-01-16 02:05:51.095000\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning when creating estimated parameters. The parameters will be ordered such that all parameters (excluding initial states) defined by a single variable will be stored before those represented by a list of variables. The parameter order will be different than those in your parameter settings. It is recommended that you check the parameter order by calling the print_parameter_names(Python)/printEstimatableParameterEntries(C++) function\n", "Warning when creating estimated parameters. The parameters will be ordered such that all parameters (excluding initial states) defined by a single variable will be stored before those represented by a list of variables. The parameter order will be different than those in your parameter settings. It is recommended that you check the parameter order by calling the print_parameter_names(Python)/printEstimatableParameterEntries(C++) function\n", "Warning when creating estimated parameters. The parameters will be ordered such that all parameters (excluding initial states) defined by a single variable will be stored before those represented by a list of variables. The parameter order will be different than those in your parameter settings. It is recommended that you check the parameter order by calling the print_parameter_names(Python)/printEstimatableParameterEntries(C++) function\n", "Warning when creating estimated parameters. The parameters will be ordered such that all parameters (excluding initial states) defined by a single variable will be stored before those represented by a list of variables. The parameter order will be different than those in your parameter settings. It is recommended that you check the parameter order by calling the print_parameter_names(Python)/printEstimatableParameterEntries(C++) function\n", "Warning when creating estimated parameters. The parameters will be ordered such that all parameters (excluding initial states) defined by a single variable will be stored before those represented by a list of variables. The parameter order will be different than those in your parameter settings. It is recommended that you check the parameter order by calling the print_parameter_names(Python)/printEstimatableParameterEntries(C++) function\n", "Warning when creating estimated parameters. The parameters will be ordered such that all parameters (excluding initial states) defined by a single variable will be stored before those represented by a list of variables. The parameter order will be different than those in your parameter settings. It is recommended that you check the parameter order by calling the print_parameter_names(Python)/printEstimatableParameterEntries(C++) function\n", "Warning when creating estimated parameters. The parameters will be ordered such that all parameters (excluding initial states) defined by a single variable will be stored before those represented by a list of variables. The parameter order will be different than those in your parameter settings. It is recommended that you check the parameter order by calling the print_parameter_names(Python)/printEstimatableParameterEntries(C++) function\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Arc 0 interval:\n", "2012-01-01 03:19:08.148901283740997 2012-01-04 01:59:21.315985918045044\n", "Observation time bounds: 2012-01-01 23:59:54.683925867080688 - 2012-01-03 21:32:22.683982312679291\n", "Propagation time limits:\n", "2012-01-01 22:59:54.683925867080688 2012-01-03 22:32:22.683982312679291\n", "Number of parameters to estimate: 159\n", "Nominal values +- apriori:\n", "-3.632079e+06 +- 1.000000e+03\n", "1.445152e+05 +- 1.000000e+03\n", "3.343499e+05 +- 1.000000e+03\n", "-1.840645e+02 +- 1.000000e-01\n", "1.607799e+03 +- 1.000000e-01\n", "-3.026222e+03 +- 1.000000e-01\n", "1.000000e+00 +- 2.000000e+00\n", "1.000000e+00 +- 2.000000e+00\n", "1.000000e+00 +- 2.000000e+00\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "\n", "\n", "Arc 5 interval:\n", "2012-01-16 02:23:50.536327004432678 2012-01-19 02:54:03.269412040710449\n", "Observation time bounds: 2012-01-17 06:42:44.684359610080719 - 2012-01-19 02:10:03.684411764144897\n", "Propagation time limits:\n", "2012-01-17 05:42:44.684359610080719 2012-01-19 03:10:03.684411764144897\n", "Number of parameters to estimate: 153\n", "Nominal values +- apriori:\n", "-2.990812e+06 +- 1.000000e+03\n", "9.477638e+05 +- 1.000000e+03\n", "-1.834971e+06 +- 1.000000e+03\n", "1.962390e+03 +- 1.000000e-01\n", "1.546728e+03 +- 1.000000e-01\n", "-2.366223e+03 +- 1.000000e-01\n", "1.000000e+00 +- 2.000000e+00\n", "1.000000e+00 +- 2.000000e+00\n", "1.000000e+00 +- 2.000000e+00\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "\n", "\n", "Arc 3 interval:\n", "2012-01-10 02:23:50.723157346248627 2012-01-13 02:54:04.288242876529694\n", "Observation time bounds: 2012-01-11 00:53:36.684184074401855 - 2012-01-13 02:11:17.684242665767670\n", "Propagation time limits:\n", "2012-01-10 23:53:36.684184074401855 2012-01-13 03:11:17.684242665767670\n", "Number of parameters to estimate: 171\n", "Nominal values +- apriori:\n", "-3.618094e+06 +- 1.000000e+03\n", "-2.405360e+05 +- 1.000000e+03\n", "4.441781e+05 +- 1.000000e+03\n", "-4.448556e+02 +- 1.000000e-01\n", "1.692201e+03 +- 1.000000e-01\n", "-2.949075e+03 +- 1.000000e-01\n", "1.000000e+00 +- 2.000000e+00\n", "1.000000e+00 +- 2.000000e+00\n", "1.000000e+00 +- 2.000000e+00\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "\n", "\n", "Arc 2 interval:\n", "2012-01-07 03:24:20.591073334217072 2012-01-10 02:04:33.297156989574432\n", "Observation time bounds: 2012-01-07 18:19:04.684092700481415 - 2012-01-10 02:04:32.684157609939575\n", "Propagation time limits:\n", "2012-01-07 17:19:04.684092700481415 2012-01-10 03:04:32.684157609939575\n", "Number of parameters to estimate: 189\n", "Nominal values +- apriori:\n", "-3.524685e+06 +- 1.000000e+03\n", "-3.734058e+05 +- 1.000000e+03\n", "8.949253e+05 +- 1.000000e+03\n", "-8.737418e+02 +- 1.000000e-01\n", "1.638276e+03 +- 1.000000e-01\n", "-2.881875e+03 +- 1.000000e-01\n", "1.000000e+00 +- 2.000000e+00\n", "1.000000e+00 +- 2.000000e+00\n", "1.000000e+00 +- 2.000000e+00\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 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1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "\n", "\n", "Arc 6 interval:\n", "2012-01-19 03:18:24.015412509441376 2012-01-22 01:58:37.260494768619537\n", "Observation time bounds: 2012-01-19 14:07:29.684425473213196 - 2012-01-22 01:58:36.684495270252228\n", "Propagation time limits:\n", "2012-01-19 13:07:29.684425473213196 2012-01-22 02:58:36.684495270252228\n", "Number of parameters to estimate: 207\n", "Nominal values +- apriori:\n", "4.980562e+05 +- 1.000000e+03\n", "-1.906088e+06 +- 1.000000e+03\n", "3.119865e+06 +- 1.000000e+03\n", "-3.351023e+03 +- 1.000000e-01\n", "-4.240505e+02 +- 1.000000e-01\n", "2.667211e+02 +- 1.000000e-01\n", "1.000000e+00 +- 2.000000e+00\n", "1.000000e+00 +- 2.000000e+00\n", "1.000000e+00 +- 2.000000e+00\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 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1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "\n", "\n", "Arc 4 interval:\n", "2012-01-13 03:16:44.296243309974670 2012-01-16 02:06:57.279326677322388\n", "Observation time bounds: 2012-01-13 14:09:11.684256434440613 - 2012-01-16 02:06:56.684327244758606\n", "Propagation time limits:\n", "2012-01-13 13:09:11.684256434440613 2012-01-16 03:06:56.684327244758606\n", "Number of parameters to estimate: 207\n", "Nominal values +- apriori:\n", "2.828163e+05 +- 1.000000e+03\n", "-1.859956e+06 +- 1.000000e+03\n", "3.175741e+06 +- 1.000000e+03\n", "-3.377927e+03 +- 1.000000e-01\n", "-1.762637e+02 +- 1.000000e-01\n", "1.866707e+02 +- 1.000000e-01\n", "1.000000e+00 +- 2.000000e+00\n", "1.000000e+00 +- 2.000000e+00\n", "1.000000e+00 +- 2.000000e+00\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 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1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "\n", "\n", "Arc 1 interval:\n", "2012-01-04 02:26:15.889986395835876 2012-01-07 02:56:29.306072711944580\n", "Observation time bounds: 2012-01-04 17:14:30.684005498886108 - 2012-01-07 02:12:27.684072554111481\n", "Propagation time limits:\n", "2012-01-04 16:14:30.684005498886108 2012-01-07 03:12:27.684072554111481\n", "Number of parameters to estimate: 195\n", "Nominal values +- apriori:\n", "-2.681088e+06 +- 1.000000e+03\n", "-1.060313e+06 +- 1.000000e+03\n", "2.272252e+06 +- 1.000000e+03\n", "-2.304204e+03 +- 1.000000e-01\n", "1.311706e+03 +- 1.000000e-01\n", "-2.149958e+03 +- 1.000000e-01\n", "1.000000e+00 +- 2.000000e+00\n", "1.000000e+00 +- 2.000000e+00\n", "1.000000e+00 +- 2.000000e+00\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 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1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 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1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "0.000000e+00 +- 1.000000e-06\n", "\n", "\n", "\n", "\n", "Corrected values +- uncertainty:\n", "-2.990812e+06 +- 2.506051e+01\n", "9.477635e+05 +- 4.923292e+01\n", "-1.834972e+06 +- 3.914217e+01\n", "1.962391e+03 +- 3.348875e-02\n", "1.546727e+03 +- 4.017870e-02\n", "-2.366223e+03 +- 3.647531e-02\n", "1.012277e+00 +- 1.793511e+00\n", "5.901137e-01 +- 1.419568e+00\n", "1.000046e+00 +- 2.000000e+00\n", "6.052079e-08 +- 4.676519e-07\n", "1.368994e-09 +- 9.995141e-07\n", "-7.515301e-09 +- 8.084779e-07\n", "1.261990e-09 +- 9.991852e-07\n", "1.365930e-08 +- 8.311990e-07\n", "2.671646e-09 +- 9.964299e-07\n", "4.980790e-08 +- 2.988857e-07\n", "4.082714e-10 +- 9.993602e-07\n", "-2.367705e-08 +- 4.455526e-07\n", "3.282270e-09 +- 9.979931e-07\n", "9.081371e-09 +- 6.494848e-07\n", "6.982833e-09 +- 9.892055e-07\n", "3.213426e-08 +- 2.765390e-07\n", "-4.488031e-10 +- 9.994275e-07\n", "-2.752810e-08 +- 4.401885e-07\n", "3.153638e-09 +- 9.970612e-07\n", "5.712223e-09 +- 6.442837e-07\n", "6.509037e-09 +- 9.853272e-07\n", "1.953731e-08 +- 2.572012e-07\n", "4.040720e-10 +- 9.992446e-07\n", "-2.171151e-08 +- 4.436294e-07\n", "3.975420e-09 +- 9.968826e-07\n", "5.044491e-09 +- 6.517554e-07\n", "7.675491e-09 +- 9.852608e-07\n", "3.619351e-09 +- 3.376278e-07\n", "-1.265225e-10 +- 9.992011e-07\n", "-2.235672e-08 +- 6.350057e-07\n", "4.303118e-09 +- 9.972243e-07\n", "-7.067020e-09 +- 6.521271e-07\n", "8.139279e-09 +- 9.867425e-07\n", "7.821263e-09 +- 4.697224e-07\n", "-5.289558e-10 +- 9.998012e-07\n", "-1.464373e-08 +- 8.181075e-07\n", "3.481492e-09 +- 9.973304e-07\n", "-1.175295e-09 +- 7.730710e-07\n", "6.821145e-09 +- 9.866961e-07\n", "-3.754624e-09 +- 4.336101e-07\n", "-4.042634e-10 +- 9.997839e-07\n", "-1.328358e-08 +- 8.144863e-07\n", "3.344826e-09 +- 9.974784e-07\n", "-2.156486e-09 +- 8.079162e-07\n", "6.271190e-09 +- 9.869549e-07\n", "9.039766e-09 +- 5.285214e-07\n", "3.176603e-10 +- 9.997681e-07\n", "-1.551531e-08 +- 7.964402e-07\n", "3.723425e-09 +- 9.973988e-07\n", "-7.328174e-09 +- 8.141860e-07\n", "6.590660e-09 +- 9.872863e-07\n", "1.636427e-08 +- 3.790809e-07\n", "-1.267972e-10 +- 9.999489e-07\n", "-1.608750e-08 +- 7.940256e-07\n", "3.941758e-09 +- 9.971602e-07\n", "-9.548130e-09 +- 8.085227e-07\n", "6.951477e-09 +- 9.867718e-07\n", "3.843517e-08 +- 3.167450e-07\n", "-1.578340e-09 +- 9.990074e-07\n", "-8.609885e-09 +- 5.102280e-07\n", "2.205967e-09 +- 9.975191e-07\n", "1.004419e-08 +- 7.399311e-07\n", "4.066607e-09 +- 9.883169e-07\n", "6.671255e-08 +- 4.684163e-07\n", "2.279990e-10 +- 9.989215e-07\n", "-1.130426e-08 +- 7.115153e-07\n", "2.049997e-09 +- 9.978050e-07\n", "2.070199e-08 +- 7.537868e-07\n", "2.382021e-09 +- 9.891779e-07\n", "7.383824e-08 +- 6.624763e-07\n", "-4.661072e-11 +- 9.999963e-07\n", "-8.535292e-09 +- 8.064561e-07\n", "2.193921e-09 +- 9.974213e-07\n", "1.317660e-08 +- 8.190717e-07\n", "2.709750e-09 +- 9.873771e-07\n", "8.648878e-08 +- 4.643170e-07\n", "-2.416635e-12 +- 9.999430e-07\n", "-9.372736e-09 +- 8.063114e-07\n", "2.239421e-09 +- 9.974912e-07\n", "1.208151e-08 +- 8.280331e-07\n", "2.774844e-09 +- 9.872013e-07\n", "8.037709e-08 +- 3.647935e-07\n", "-3.706696e-10 +- 9.991516e-07\n", "-1.256365e-08 +- 5.360252e-07\n", "2.071885e-09 +- 9.976764e-07\n", "1.346174e-08 +- 7.579426e-07\n", "2.273830e-09 +- 9.876263e-07\n", "6.451749e-08 +- 3.221078e-07\n", "-4.204857e-10 +- 9.987160e-07\n", "-1.668189e-08 +- 5.161279e-07\n", "1.742873e-09 +- 9.977569e-07\n", "1.397245e-08 +- 6.006862e-07\n", "1.744838e-09 +- 9.869620e-07\n", "4.245265e-08 +- 2.995860e-07\n", "-2.134951e-09 +- 9.986334e-07\n", "-2.793421e-08 +- 5.198176e-07\n", "-5.093363e-10 +- 9.977372e-07\n", "6.006179e-09 +- 5.750335e-07\n", "-2.395339e-09 +- 9.865424e-07\n", "2.698110e-08 +- 3.631569e-07\n", "-1.333871e-09 +- 9.987330e-07\n", "-2.123361e-08 +- 7.359712e-07\n", "-3.618282e-09 +- 9.981419e-07\n", "1.854115e-09 +- 5.834385e-07\n", "-8.880288e-09 +- 9.884868e-07\n", "9.526848e-09 +- 6.578758e-07\n", "5.034082e-10 +- 9.997171e-07\n", "-1.079671e-08 +- 9.082965e-07\n", "-3.990801e-09 +- 9.981461e-07\n", "1.081743e-09 +- 9.092561e-07\n", "-1.009126e-08 +- 9.888818e-07\n", "1.199340e-08 +- 8.516361e-07\n", "1.915534e-10 +- 9.999956e-07\n", "-1.123641e-08 +- 9.083262e-07\n", "-3.956718e-09 +- 9.980874e-07\n", "-1.137956e-09 +- 9.070260e-07\n", "-9.870947e-09 +- 9.881278e-07\n", "1.233042e-08 +- 8.997910e-07\n", "2.130390e-10 +- 9.999945e-07\n", "-1.123252e-08 +- 9.082272e-07\n", "-4.084510e-09 +- 9.979651e-07\n", "-1.414982e-09 +- 9.068605e-07\n", "-9.946844e-09 +- 9.879452e-07\n", "1.266935e-08 +- 8.446616e-07\n", "2.084379e-10 +- 9.999948e-07\n", "-1.124336e-08 +- 9.082921e-07\n", "-4.295093e-09 +- 9.977542e-07\n", "-1.509353e-09 +- 9.074118e-07\n", "-9.947108e-09 +- 9.879463e-07\n", "1.324110e-08 +- 6.607132e-07\n", "5.341877e-10 +- 9.999041e-07\n", "-1.093191e-08 +- 9.085860e-07\n", "-4.144334e-09 +- 9.976220e-07\n", "-1.127403e-09 +- 9.100839e-07\n", "-9.852510e-09 +- 9.882029e-07\n", "3.410035e-08 +- 3.749991e-07\n", "3.021642e-09 +- 9.986248e-07\n", "-3.022601e-08 +- 7.326995e-07\n", "-7.277602e-10 +- 9.989657e-07\n", "1.115252e-08 +- 6.698023e-07\n", "-4.087867e-09 +- 9.940586e-07\n", "-1.208300e-09 +- 9.936065e-07\n", "1.267967e-10 +- 9.996023e-07\n", "-1.294801e-10 +- 9.997252e-07\n", "-4.220674e-12 +- 9.999846e-07\n", "9.644447e-10 +- 9.938319e-07\n", "-8.537762e-11 +- 9.996296e-07\n", "Finished processing arc 5\n", "\n", "\n", "Corrected values +- uncertainty:\n", "-3.632080e+06 +- 9.311510e+00\n", "1.445151e+05 +- 2.248602e+01\n", "3.343497e+05 +- 3.531354e+01\n", "-1.840643e+02 +- 3.393532e-02\n", "1.607799e+03 +- 6.116732e-02\n", "-3.026222e+03 +- 3.144925e-02\n", "9.974292e-01 +- 1.784387e+00\n", "6.184629e-01 +- 1.450972e+00\n", "1.000135e+00 +- 2.000000e+00\n", "3.791650e-08 +- 4.174972e-07\n", "3.541853e-10 +- 9.997567e-07\n", "-2.709218e-08 +- 7.063889e-07\n", "1.564919e-10 +- 9.995365e-07\n", "5.621346e-09 +- 8.493287e-07\n", "1.938712e-10 +- 9.993494e-07\n", "2.325452e-08 +- 2.714763e-07\n", "1.032142e-09 +- 9.995923e-07\n", "-1.857825e-08 +- 5.619258e-07\n", "1.399457e-09 +- 9.992027e-07\n", "1.099283e-08 +- 5.397667e-07\n", "2.343031e-09 +- 9.975181e-07\n", "1.053844e-08 +- 2.790373e-07\n", "3.489282e-10 +- 9.995893e-07\n", "-1.266815e-08 +- 6.198125e-07\n", "2.442915e-09 +- 9.988084e-07\n", "3.728948e-10 +- 5.279359e-07\n", "4.548413e-09 +- 9.955375e-07\n", "1.123636e-09 +- 3.132207e-07\n", "9.269911e-11 +- 9.998632e-07\n", "-1.550375e-08 +- 5.318232e-07\n", "2.854946e-09 +- 9.984456e-07\n", "4.101972e-09 +- 5.184153e-07\n", "5.713606e-09 +- 9.934698e-07\n", "2.473379e-09 +- 2.282290e-07\n", "-2.917048e-10 +- 9.992226e-07\n", "-1.664738e-08 +- 5.579590e-07\n", "2.924043e-09 +- 9.983035e-07\n", "-2.101286e-09 +- 5.155523e-07\n", "6.052349e-09 +- 9.929836e-07\n", "9.773367e-10 +- 2.099287e-07\n", "-1.816152e-09 +- 9.991524e-07\n", "-2.438499e-08 +- 4.882755e-07\n", "1.135820e-09 +- 9.978684e-07\n", "-7.622531e-10 +- 4.476732e-07\n", "3.378398e-09 +- 9.903821e-07\n", "1.274128e-08 +- 2.220832e-07\n", "-9.256638e-10 +- 9.991551e-07\n", "-1.880019e-08 +- 5.005138e-07\n", "-7.235772e-10 +- 9.976852e-07\n", "1.078966e-08 +- 4.682260e-07\n", "-1.146016e-09 +- 9.891592e-07\n", "3.092410e-08 +- 2.433420e-07\n", "1.136964e-09 +- 9.993807e-07\n", "-1.715516e-08 +- 5.031389e-07\n", "1.540460e-10 +- 9.976162e-07\n", "1.303421e-08 +- 4.588712e-07\n", "-7.980123e-10 +- 9.884725e-07\n", "5.358895e-08 +- 2.628267e-07\n", "1.439359e-09 +- 9.991302e-07\n", "-9.258635e-09 +- 4.715838e-07\n", "2.282522e-09 +- 9.976768e-07\n", "1.733344e-08 +- 4.695409e-07\n", "3.138704e-09 +- 9.891700e-07\n", "6.092835e-08 +- 2.837990e-07\n", "-1.667441e-09 +- 9.990785e-07\n", "-2.568320e-08 +- 4.739656e-07\n", "1.450308e-09 +- 9.979263e-07\n", "2.219671e-08 +- 4.577945e-07\n", "3.357143e-09 +- 9.896380e-07\n", "6.607894e-08 +- 3.818481e-07\n", "1.426498e-09 +- 9.991350e-07\n", "-2.394830e-08 +- 6.997581e-07\n", "2.092063e-09 +- 9.985906e-07\n", "1.820607e-08 +- 5.220697e-07\n", "2.658368e-09 +- 9.910183e-07\n", "7.537204e-08 +- 5.169966e-07\n", "-1.651268e-10 +- 9.999812e-07\n", "-1.480301e-08 +- 7.929709e-07\n", "2.656721e-09 +- 9.985750e-07\n", "1.591555e-08 +- 7.834747e-07\n", "4.875850e-09 +- 9.906024e-07\n", "5.946000e-08 +- 4.073182e-07\n", "-2.926775e-10 +- 9.999624e-07\n", "-1.495139e-08 +- 7.950073e-07\n", "2.585876e-09 +- 9.985663e-07\n", "1.669625e-08 +- 7.945049e-07\n", "4.853583e-09 +- 9.905498e-07\n", "3.854410e-08 +- 3.222857e-07\n", "-1.883756e-10 +- 9.996720e-07\n", "-2.293252e-08 +- 5.771464e-07\n", "2.638350e-09 +- 9.984976e-07\n", "5.553735e-09 +- 6.634955e-07\n", "4.223534e-09 +- 9.910632e-07\n", "2.386322e-08 +- 2.723791e-07\n", "-4.318709e-11 +- 9.995169e-07\n", "-2.389855e-08 +- 5.559102e-07\n", "2.811106e-09 +- 9.984550e-07\n", "7.133145e-09 +- 5.452342e-07\n", "4.564405e-09 +- 9.914710e-07\n", "8.670082e-09 +- 2.626304e-07\n", "-1.248173e-09 +- 9.994998e-07\n", "-2.160467e-08 +- 5.669726e-07\n", "1.575734e-09 +- 9.985671e-07\n", "-1.538879e-09 +- 5.691477e-07\n", "2.603973e-09 +- 9.920712e-07\n", "7.416793e-09 +- 2.918717e-07\n", "1.335427e-10 +- 9.999259e-07\n", "-1.227267e-08 +- 5.734094e-07\n", "1.125662e-09 +- 9.984671e-07\n", "8.600209e-10 +- 5.436857e-07\n", "9.508002e-10 +- 9.914622e-07\n", "1.932417e-09 +- 2.590005e-07\n", "-7.953781e-10 +- 9.995158e-07\n", "-1.081096e-08 +- 5.569521e-07\n", "6.080923e-10 +- 9.986006e-07\n", "3.987823e-09 +- 5.983362e-07\n", "5.402385e-10 +- 9.921144e-07\n", "-1.869229e-09 +- 2.676975e-07\n", "-1.421598e-09 +- 9.994964e-07\n", "-2.300772e-08 +- 5.929114e-07\n", "-1.221709e-09 +- 9.986111e-07\n", "2.705574e-10 +- 5.300491e-07\n", "-2.891189e-09 +- 9.923980e-07\n", "9.375293e-09 +- 4.014680e-07\n", "-5.814445e-10 +- 9.995445e-07\n", "-1.515215e-08 +- 8.032719e-07\n", "-2.895136e-09 +- 9.987256e-07\n", "1.623729e-09 +- 6.919342e-07\n", "-6.543290e-09 +- 9.930828e-07\n", "2.723306e-08 +- 6.250577e-07\n", "1.696217e-10 +- 9.999962e-07\n", "-1.994353e-08 +- 7.716304e-07\n", "-3.133401e-09 +- 9.985813e-07\n", "4.383618e-09 +- 7.118348e-07\n", "-7.392362e-09 +- 9.929440e-07\n", "4.898333e-08 +- 4.170369e-07\n", "-9.746312e-10 +- 9.995549e-07\n", "-1.177305e-08 +- 6.932784e-07\n", "-4.725056e-09 +- 9.987297e-07\n", "2.315989e-08 +- 7.424900e-07\n", "-9.218361e-09 +- 9.941017e-07\n", "6.136841e-08 +- 3.202062e-07\n", "1.274505e-09 +- 9.994943e-07\n", "-2.014600e-08 +- 5.504245e-07\n", "-4.244414e-09 +- 9.990033e-07\n", "2.541675e-08 +- 5.498493e-07\n", "-9.512425e-09 +- 9.953478e-07\n", "6.940703e-08 +- 3.994592e-07\n", "2.238410e-09 +- 9.995338e-07\n", "-2.088905e-08 +- 7.802338e-07\n", "-1.594955e-09 +- 9.996665e-07\n", "1.768289e-08 +- 5.464668e-07\n", "-5.265762e-09 +- 9.979120e-07\n", "-4.439216e-09 +- 9.953775e-07\n", "6.411938e-10 +- 9.999104e-07\n", "4.053366e-10 +- 9.998501e-07\n", "2.012744e-11 +- 9.999975e-07\n", "4.359783e-09 +- 9.955134e-07\n", "-6.163999e-10 +- 9.999161e-07\n", "Finished processing arc 0\n", "\n", "\n", "Corrected values +- uncertainty:\n", "-3.618094e+06 +- 1.201022e+01\n", "-2.405365e+05 +- 2.160938e+01\n", "4.441785e+05 +- 3.571385e+01\n", "-4.448560e+02 +- 3.382145e-02\n", "1.692201e+03 +- 5.662958e-02\n", "-2.949075e+03 +- 3.090716e-02\n", "9.877675e-01 +- 1.773573e+00\n", "6.406839e-01 +- 1.495341e+00\n", "1.000104e+00 +- 2.000000e+00\n", "7.027936e-08 +- 4.892128e-07\n", "1.054484e-09 +- 9.995488e-07\n", "-1.176203e-08 +- 8.134993e-07\n", "4.316299e-10 +- 9.993382e-07\n", "1.321208e-08 +- 8.549229e-07\n", "7.344295e-10 +- 9.989308e-07\n", "6.040663e-08 +- 5.295359e-07\n", "8.217955e-10 +- 9.998414e-07\n", "-2.125896e-08 +- 8.564120e-07\n", "2.091344e-09 +- 9.991016e-07\n", "1.550716e-08 +- 8.505244e-074.582685e-08 +- 8.387106e-07\n", "4.406228e-09 +- 9.957436e-07\n", "\n", "-1.327927e-10 +- 9.999971e-07\n", "-2.213902e-08 +- 8.546412e-07\n", "2.316873e-09 +- 9.990233e-07\n", "1.116364e-08 +- 8.440558e-07\n", "5.342087e-09 +- 9.951362e-07\n", "2.552383e-08 +- 4.902080e-07\n", "-3.121865e-10 +- 9.998434e-07\n", "-2.157334e-08 +- 8.539153e-07\n", "2.319540e-09 +- 9.988996e-07\n", "1.128166e-08 +- 8.543033e-07\n", "5.238292e-09 +- 9.951754e-07\n", "1.343958e-08 +- 2.893256e-07\n", "-1.336517e-10 +- 9.991812e-07\n", "-1.704974e-08 +- 5.949568e-07\n", "2.370585e-09 +- 9.984629e-07\n", "3.059871e-10 +- 6.076704e-07\n", "4.987258e-09 +- 9.935698e-07\n", "4.127031e-09 +- 2.567377e-07\n", "-2.948620e-10 +- 9.990422e-07\n", "-1.620296e-08 +- 5.790476e-07\n", "2.216669e-09 +- 9.982972e-07\n", "1.364815e-09 +- 5.052978e-07\n", "4.459836e-09 +- 9.910145e-07\n", "-1.503229e-10 +- 3.212068e-07\n", "-4.201665e-10 +- 9.990883e-07\n", "-1.384870e-08 +- 7.379051e-07\n", "1.818454e-09 +- 9.986999e-07\n", "3.511758e-09 +- 5.087946e-07\n", "3.744116e-09 +- 9.910145e-07\n", "-1.782644e-09 +- 5.945633e-07\n", "1.762740e-10 +- 9.998063e-07\n", "-1.143184e-08 +- 9.033221e-07\n", "2.014640e-09 +- 9.987561e-07\n", "9.764590e-09 +- 8.851440e-07\n", "3.865491e-09 +- 9.909246e-07\n", "1.062994e-08 +- 8.487499e-07\n", "-1.005180e-10 +- 9.999943e-07\n", "-1.166192e-08 +- 9.018378e-07\n", "2.125367e-09 +- 9.986488e-07\n", "7.147490e-09 +- 8.824608e-07\n", "4.131675e-09 +- 9.905492e-07\n", "2.087116e-08 +- 8.569021e-07\n", "-1.017102e-10 +- 9.999943e-07\n", "-1.174592e-08 +- 9.015061e-07\n", "2.192356e-09 +- 9.985411e-07\n", "6.997076e-09 +- 8.829445e-07\n", "4.100783e-09 +- 9.906433e-07\n", "3.265838e-08 +- 6.391192e-07\n", "2.488587e-10 +- 9.998589e-07\n", "-1.046080e-08 +- 9.015525e-07\n", "2.550882e-09 +- 9.985660e-07\n", "8.006484e-09 +- 8.926781e-07\n", "4.226284e-09 +- 9.909787e-075.336850e-08 +- 6.029497e-07\n", "\n", "1.114649e-10 +- 9.998434e-07\n", "-1.149791e-08 +- 8.808925e-07\n", "3.110490e-09 +- 9.982723e-07\n", "2.642738e-08 +- 8.762509e-07\n", "5.448885e-09 +- 9.909713e-07\n", "6.100295e-08 +- 8.619613e-07\n", "-1.355463e-10 +- 9.999942e-07\n", "-1.249176e-08 +- 8.777030e-07\n", "3.342771e-09 +- 9.981524e-07\n", "2.273346e-08 +- 8.739305e-07\n", "5.744684e-09 +- 9.903258e-07\n", "6.615881e-08 +- 6.823452e-07\n", "-1.382718e-10 +- 9.999945e-07\n", "-1.347234e-08 +- 8.791651e-07\n", "3.327067e-09 +- 9.981792e-07\n", "2.182791e-08 +- 8.740561e-07\n", "5.793318e-09 +- 9.902114e-07\n", "6.115327e-08 +- 4.975362e-07\n", "-3.855916e-10 +- 9.993302e-07\n", "-1.806564e-08 +- 7.963461e-07\n", "3.176859e-09 +- 9.980443e-07\n", "1.858262e-08 +- 7.757027e-07\n", "5.598102e-09 +- 9.900786e-07\n", "4.680398e-08 +- 2.779939e-07\n", "-4.464644e-10 +- 9.988449e-07\n", "-1.672410e-08 +- 5.602364e-07\n", "3.003317e-09 +- 9.977980e-07\n", "1.063859e-08 +- 4.990528e-07\n", "5.045171e-09 +- 9.881282e-07\n", "2.580174e-08 +- 2.408107e-07\n", "-1.750813e-09 +- 9.980770e-07\n", "-2.367958e-08 +- 5.057339e-07\n", "1.345633e-09 +- 9.973065e-07\n", "9.014543e-09 +- 4.470598e-07\n", "2.183122e-09 +- 9.863756e-07\n", "1.219319e-08 +- 2.257692e-07\n", "-1.411397e-09 +- 9.980520e-07\n", "-2.465844e-08 +- 5.202047e-07\n", "-8.084870e-10 +- 9.971617e-07\n", "3.119287e-09 +- 4.291056e-07\n", "-2.675724e-09 +- 9.852378e-07\n", "2.885750e-09 +- 2.367454e-07\n", "7.858563e-11 +- 9.987609e-07\n", "-2.044011e-09 +- 4.693690e-07-1.238703e-08 +- 5.459059e-07\n", "-1.503229e-09 +- 9.972286e-07\n", "\n", "-4.842870e-09 +- 9.852907e-07\n", "2.167466e-09 +- 2.495374e-07\n", "-6.028117e-10 +- 9.988746e-07\n", "-8.244002e-09 +- 5.735022e-07\n", "-2.330808e-09 +- 9.977093e-07\n", "8.182529e-10 +- 4.931683e-07\n", "-6.091119e-09 +- 9.866804e-07\n", "-6.083422e-10 +- 3.166330e-07\n", "-7.170201e-10 +- 9.989569e-07\n", "-1.421105e-08 +- 7.216844e-07\n", "-3.552989e-09 +- 9.983459e-07\n", "5.461931e-09 +- 5.143191e-07\n", "-8.390182e-09 +- 9.896048e-07\n", "3.349046e-09 +- 4.929337e-07\n", "5.284723e-10 +- 9.997715e-07\n", "-1.423316e-08 +- 8.561031e-07\n", "-3.826434e-09 +- 9.985117e-07\n", "8.281609e-09 +- 8.459983e-07\n", "-9.355296e-09 +- 9.914831e-07\n", "1.434279e-08 +- 8.278865e-07\n", "2.178836e-10 +- 9.999950e-07\n", "-1.453292e-08 +- 8.541171e-07\n", "-3.916012e-09 +- 9.983802e-07\n", "5.774452e-09 +- 8.408831e-07\n", "-9.025455e-09 +- 9.914715e-07\n", "2.391672e-08 +- 4.925021e-07\n", "4.200042e-10 +- 9.998013e-07\n", "-1.360513e-08 +- 8.547563e-07\n", "-3.869578e-09 +- 9.985825e-07\n", "6.408996e-09 +- 8.516689e-07\n", "-8.843750e-09 +- 9.921839e-07\n", "4.573945e-08 +- 3.426611e-07\n", "1.301836e-09 +- 9.994370e-07\n", "-1.615166e-08 +- 5.959039e-07\n", "-2.800641e-09 +- 9.987567e-07\n", "1.320722e-08 +- 6.532718e-07\n", "-6.656234e-09 +- 9.944539e-07\n", "6.227924e-08 +- 3.941694e-07\n", "1.432453e-09 +- 9.992852e-07\n", "-1.783462e-08 +- 7.958261e-07\n", "-1.143272e-09 +- 9.994870e-07\n", "1.537111e-08 +- 5.624279e-07\n", "-3.547791e-09 +- 9.972313e-07\n", "-1.933185e-09 +- 9.970664e-07\n", "4.861875e-10 +- 9.998453e-07\n", "7.338137e-11 +- 9.998325e-07\n", "5.734041e-11 +- 9.999943e-07\n", "1.949530e-09 +- 9.970129e-07\n", "-4.707975e-10 +- 9.998550e-07\n", "Finished processing arc 3\n", "\n", "\n", "Corrected values +- uncertainty:\n", "-3.524685e+06 +- 1.697394e+01\n", "-3.734066e+05 +- 2.103646e+01\n", "8.949259e+05 +- 3.451407e+01\n", "-8.737426e+02 +- 3.348223e-02\n", "1.638276e+03 +- 5.494891e-02\n", "-2.881875e+03 +- 2.902385e-02\n", "1.014698e+00 +- 1.745871e+00\n", "5.081813e-01 +- 1.285930e+00\n", "1.000132e+00 +- 2.000000e+00\n", "4.753411e-08 +- 5.700587e-07\n", "3.025261e-09 +- 9.995246e-07\n", "-2.178243e-08 +- 8.165352e-07\n", "3.171018e-09 +- 9.993451e-07\n", "-4.077158e-09 +- 8.884821e-07\n", "3.653192e-09 +- 9.991262e-07\n", "5.922282e-08 +- 6.153339e-07\n", "-2.188497e-10 +- 9.999972e-07\n", "-1.339238e-08 +- 8.105844e-07\n", "4.620675e-09 +- 9.989346e-07\n", "1.766499e-08 +- 8.040713e-07\n", "8.621574e-09 +- 9.959798e-07\n", "6.031216e-08 +- 4.622897e-07\n", "-8.580589e-11 +- 9.999638e-07\n", "-1.386971e-08 +- 8.124040e-07\n", "4.741481e-09 +- 9.989557e-07\n", "1.713833e-08 +- 8.196051e-07\n", "8.745557e-09 +- 9.959286e-07\n", "5.177293e-08 +- 4.958411e-07\n", "1.650058e-09 +- 9.994020e-07\n", "-2.382783e-08 +- 8.031911e-07\n", "6.723516e-09 +- 9.989534e-07\n", "5.566625e-09 +- 7.853095e-07\n", "1.168310e-08 +- 9.957898e-07\n", "5.085417e-08 +- 6.092030e-07\n", "-3.793838e-10 +- 9.999932e-07\n", "-1.900560e-08 +- 8.627276e-07\n", "7.363520e-09 +- 9.987131e-07\n", "5.593870e-09 +- 8.615285e-07\n", "1.450368e-08 +- 9.933494e-07\n", "3.340207e-08 +- 8.395794e-07\n", "-3.588195e-10 +- 9.999959e-07\n", "-1.888546e-08 +- 8.624479e-07\n", "7.777816e-09 +- 9.985711e-07\n", "6.700146e-09 +- 8.650767e-07\n", "1.446006e-08 +- 9.933722e-07\n", "1.597915e-08 +- 5.135523e-07\n", "-3.186272e-10 +- 9.999509e-07\n", "-1.883654e-08 +- 8.623429e-07\n", "8.270474e-09 +- 9.984188e-07\n", "7.457161e-09 +- 8.707747e-07\n", "1.435092e-08 +- 9.935085e-07\n", "4.742136e-09 +- 3.018024e-07\n", "3.905034e-10 +- 9.995225e-07\n", "-1.613015e-08 +- 6.167741e-07\n", "9.246555e-09 +- 9.982072e-07\n", "6.349611e-09 +- 6.818683e-07\n", "1.514897e-08 +- 9.933158e-07\n", "7.613319e-10 +- 2.539983e-07\n", "4.592619e-10 +- 9.992298e-07\n", "-1.497879e-08 +- 5.879910e-07\n", "1.055132e-08 +- 9.980724e-07\n", "2.966924e-10 +- 5.117192e-07\n", "1.773688e-08 +- 9.918956e-07\n", "-9.760658e-10 +- 2.516979e-07\n", "-1.273702e-09 +- 9.992031e-07\n", "-1.204619e-08 +- 5.863998e-07\n", "1.020160e-08 +- 9.978922e-07\n", "3.246657e-09 +- 4.865815e-07\n", "1.826129e-08 +- 9.901924e-07\n", "-8.787419e-09 +- 2.526363e-07\n", "-1.528198e-09 +- 9.991852e-07\n", "-2.884150e-08 +- 5.851181e-07\n", "9.271816e-09 +- 9.977408e-07\n", "-3.874446e-09 +- 4.888647e-07\n", "1.555439e-08 +- 9.889825e-07\n", "5.451217e-09 +- 2.780968e-07\n", "-7.628420e-10 +- 9.991926e-07\n", "-2.185906e-08 +- 6.232699e-07\n", "8.397525e-09 +- 9.976753e-07\n", "3.700319e-09 +- 4.967744e-07\n", "1.292038e-08 +- 9.884421e-07\n", "1.875017e-08 +- 3.127113e-07\n", "-3.663911e-10 +- 9.997003e-07\n", "-1.759497e-08 +- 5.871664e-07\n", "8.396698e-09 +- 9.973627e-07\n", "-9.499018e-10 +- 5.079288e-07\n", "1.193304e-08 +- 9.872686e-07\n", "4.188044e-08 +- 2.927436e-07\n", "-8.581828e-10 +- 9.991760e-07\n", "-1.320740e-08 +- 6.049516e-07\n", "7.977126e-09 +- 9.973296e-07\n", "1.610195e-08 +- 5.227965e-07\n", "1.089617e-08 +- 9.878059e-07\n", "4.649650e-08 +- 3.030243e-07\n", "-1.053097e-09 +- 9.991598e-07\n", "-2.470457e-08 +- 5.661779e-07\n", "7.404861e-09 +- 9.970752e-07\n", "1.116283e-08 +- 5.070617e-07\n", "9.220130e-09 +- 9.868947e-07\n", "5.536507e-08 +- 3.078453e-07\n", "-8.056332e-10 +- 9.991317e-07\n", "-1.636357e-08 +- 5.536122e-07\n", "6.267684e-09 +- 9.970831e-07\n", "6.793952e-09 +- 5.059868e-07\n", "7.068354e-09 +- 9.859097e-07\n", "6.673545e-08 +- 3.012335e-07\n", "-2.244270e-09 +- 9.987420e-07\n", "-1.213874e-08 +- 5.364804e-07\n", "3.874151e-09 +- 9.973035e-07\n", "1.346209e-08 +- 4.719258e-07\n", "3.218740e-09 +- 9.853934e-07\n", "5.383279e-08 +- 2.935471e-07\n", "-2.151819e-09 +- 9.985399e-07\n", "-1.494762e-08 +- 5.409970e-07\n", "1.100518e-09 +- 9.974316e-07\n", "1.453283e-08 +- 4.705818e-07\n", "-3.052819e-09 +- 9.846700e-07\n", "3.486994e-08 +- 2.860348e-07\n", "-1.028441e-09 +- 9.993558e-07\n", "-2.540794e-08 +- 5.572205e-07\n", "-9.240054e-10 +- 9.969557e-07\n", "-7.265186e-10 +- 4.756113e-07\n", "-8.594068e-09 +- 9.826809e-07\n", "2.739158e-08 +- 2.447056e-07\n", "2.444122e-10 +- 9.989082e-07\n", "-1.553935e-08 +- 5.249983e-07\n", "-1.720386e-09 +- 9.967958e-07\n", "8.786114e-09 +- 4.948485e-07\n", "-9.914141e-09 +- 9.833904e-07\n", "8.694368e-09 +- 2.148257e-07\n", "-2.559454e-09 +- 9.984647e-07\n", "-2.081051e-08 +- 4.817325e-07\n", "-4.630049e-09 +- 9.968303e-07\n", "-4.314474e-09 +- 4.563921e-07\n", "-1.373284e-08 +- 9.842132e-07\n", "1.299471e-09 +- 2.058482e-07\n", "-5.729472e-10 +- 9.984619e-07\n", "-1.457487e-08 +- 4.804512e-07\n", "-7.175034e-09 +- 9.972063e-07\n", "-4.746704e-09 +- 4.423431e-07\n", "-2.003113e-08 +- 9.846888e-07\n", "8.857421e-10 +- 2.137221e-07\n", "-2.387256e-10 +- 9.984703e-07\n", "-8.782056e-09 +- 5.075618e-07\n", "-8.445507e-09 +- 9.979100e-07\n", "-2.204161e-09 +- 4.426800e-07\n", "-2.251589e-08 +- 9.871073e-07\n", "-2.586887e-09 +- 3.096283e-07\n", "-1.066456e-09 +- 9.989565e-07\n", "-1.670144e-08 +- 7.361276e-07\n", "-1.088223e-08 +- 9.986053e-07\n", "-7.178087e-09 +- 5.066968e-07\n", "-2.674631e-08 +- 9.907814e-07\n", "3.850423e-09 +- 5.080543e-07\n", "6.196656e-10 +- 9.999334e-07\n", "-1.913693e-08 +- 8.568462e-07\n", "-1.215873e-08 +- 9.986244e-07\n", "6.413287e-09 +- 8.460551e-07\n", "-2.931248e-08 +- 9.918436e-07\n", "2.009508e-08 +- 8.302560e-07\n", "6.970084e-10 +- 9.999954e-07\n", "-1.918580e-08 +- 8.569586e-07\n", "-1.257428e-08 +- 9.985287e-07\n", "6.545927e-09 +- 8.418135e-07\n", "-2.918345e-08 +- 9.919849e-07\n", "3.880392e-08 +- 4.954358e-07\n", "1.793056e-09 +- 9.998464e-07\n", "-1.788801e-08 +- 8.567802e-07\n", "-1.234011e-08 +- 9.985986e-07\n", "1.272265e-08 +- 8.541510e-07\n", "-2.826547e-08 +- 9.925262e-07\n", "5.401851e-08 +- 3.632091e-07\n", "7.587973e-09 +- 9.993164e-07\n", "-1.381586e-08 +- 6.868985e-07\n", "-4.906555e-09 +- 9.994480e-07\n", "4.070179e-09 +- 6.414319e-07\n", "-1.579756e-08 +- 9.964220e-07\n", "6.224722e-08 +- 4.313980e-07\n", "6.744014e-10 +- 9.998831e-07\n", "-3.255215e-09 +- 8.010336e-07\n", "-1.523291e-09 +- 9.996141e-07\n", "5.293866e-09 +- 6.082160e-07\n", "-4.150741e-09 +- 9.987514e-07\n", "1.755778e-08 +- 8.184816e-07\n", "1.143480e-09 +- 9.998276e-07\n", "-6.918402e-09 +- 9.883157e-07\n", "-1.194698e-10 +- 9.999940e-07\n", "-1.403443e-08 +- 8.764265e-07\n", "-1.649308e-09 +- 9.996582e-07\n", "Finished processing arc 2\n", "\n", "\n", "Corrected values +- uncertainty:\n", "-2.681088e+06 +- 3.161145e+01\n", "-1.060314e+06 +- 3.290167e+01\n", "2.272252e+06 +- 2.952762e+01\n", "-2.304204e+03 +- 2.659573e-02\n", "1.311706e+03 +- 4.987083e-02\n", "-2.149957e+03 +- 2.919144e-02\n", "1.001475e+00 +- 1.739435e+00\n", "5.003119e-01 +- 1.262397e+00\n", "1.000202e+00 +- 1.999999e+00\n", "5.128225e-08 +- 4.434392e-07\n", "1.353986e-09 +- 9.997784e-07\n", "4.045426e-10 +- 8.232017e-07\n", "8.812353e-10 +- 9.994162e-07\n", "1.554307e-08 +- 7.693684e-07\n", "1.054694e-09 +- 9.993464e-07\n", "5.470020e-08 +- 6.283325e-07\n", "9.156831e-10 +- 9.999113e-07\n", "-9.175385e-09 +- 8.979636e-07\n", "3.548824e-09 +- 9.991775e-07\n", "1.854271e-08 +- 8.930897e-07\n", "6.674696e-09 +- 9.974770e-07\n", "5.337619e-08 +- 8.596393e-07\n", "-2.056309e-10 +- 9.999980e-07\n", "-1.903207e-08 +- 8.933889e-07\n", "3.906769e-09 +- 9.991531e-07\n", "9.944879e-09 +- 8.904789e-07\n", "8.006566e-09 +- 9.970010e-07\n", "4.964949e-08 +- 8.721372e-07\n", "-2.007684e-10 +- 9.999980e-07\n", "-1.934660e-08 +- 8.936755e-07\n", "3.920392e-09 +- 9.991511e-07\n", "1.028934e-08 +- 8.909599e-07\n", "8.037154e-09 +- 9.969742e-07\n", "4.640350e-08 +- 6.619346e-07\n", "-1.089599e-10 +- 9.999860e-07\n", "-1.880245e-08 +- 8.948499e-07\n", "4.186443e-09 +- 9.990947e-07\n", "1.119239e-08 +- 8.916877e-07\n", "8.069661e-09 +- 9.969761e-07\n", "2.189186e-08 +- 4.860427e-07\n", "-5.963468e-12 +- 9.998958e-07\n", "-1.809323e-08 +- 7.562378e-07\n", "4.772775e-09 +- 9.988878e-07\n", "1.010542e-08 +- 6.992307e-07\n", "8.735113e-09 +- 9.965804e-07\n", "6.923938e-09 +- 2.877592e-07\n", "5.104012e-10 +- 9.995326e-07\n", "-2.824131e-08 +- 7.922013e-07\n", "5.950372e-09 +- 9.987455e-07\n", "-2.806815e-09 +- 5.112449e-07\n", "9.930504e-09 +- 9.962309e-07\n", "-1.533798e-09 +- 2.460641e-07\n", "2.158265e-09 +- 9.993585e-07\n", "-2.341693e-08 +- 6.418189e-07\n", "8.530702e-09 +- 9.984928e-07\n", "-5.455838e-09 +- 4.182278e-07\n", "1.432989e-08 +- 9.941769e-07\n", "2.961572e-09 +- 2.628085e-07\n", "-4.038931e-10 +- 9.996264e-07\n", "-5.744671e-09 +- 6.476505e-07\n", "1.004997e-08 +- 9.983184e-07\n", "-2.976045e-10 +- 3.918029e-07\n", "1.920105e-08 +- 9.922680e-07\n", "-3.742735e-10 +- 2.855218e-07\n", "-4.880861e-10 +- 9.994956e-07\n", "-1.804039e-08 +- 6.915298e-07\n", "1.050404e-08 +- 9.983855e-07\n", "2.158742e-09 +- 4.371855e-07\n", "1.975410e-08 +- 9.922330e-07\n", "1.226343e-09 +- 3.060165e-07\n", "-3.230726e-10 +- 9.998661e-07\n", "-1.860863e-08 +- 7.134335e-07\n", "1.092701e-08 +- 9.981887e-07\n", "-1.694870e-09 +- 6.277467e-07\n", "1.994992e-08 +- 9.913575e-07\n", "1.356909e-08 +- 2.865734e-07\n", "-1.496540e-09 +- 9.994777e-07\n", "-2.442918e-08 +- 7.636258e-07\n", "1.035757e-08 +- 9.983376e-07\n", "4.177875e-09 +- 6.081423e-07\n", "1.869689e-08 +- 9.919137e-07\n", "2.654829e-08 +- 2.896117e-07\n", "-1.582221e-09 +- 9.994322e-07\n", "-2.605835e-08 +- 6.509263e-07\n", "8.878805e-09 +- 9.983649e-07\n", "1.366394e-08 +- 4.162484e-07\n", "1.496199e-08 +- 9.917818e-07\n", "4.649402e-08 +- 3.057113e-07\n", "-6.828219e-10 +- 9.994175e-07\n", "-1.647954e-08 +- 6.413673e-07\n", "8.380441e-09 +- 9.983983e-07\n", "1.508724e-08 +- 4.137840e-07\n", "1.223841e-08 +- 9.919317e-07\n", "5.255148e-08 +- 3.457139e-07\n", "-1.598449e-09 +- 9.994301e-07\n", "-2.264968e-08 +- 7.016770e-07\n", "7.301872e-09 +- 9.986704e-07\n", "1.807803e-08 +- 4.361717e-07\n", "1.042865e-08 +- 9.923967e-07\n", "5.621839e-08 +- 4.367956e-07\n", "-3.011731e-10 +- 9.998723e-07\n", "-2.033596e-08 +- 8.218795e-07\n", "6.504340e-09 +- 9.987462e-07\n", "1.201472e-08 +- 7.940624e-07\n", "7.858032e-09 +- 9.921164e-07\n", "5.994371e-08 +- 6.360919e-07\n", "-1.905424e-10 +- 9.999945e-07\n", "-1.898426e-08 +- 8.141752e-07\n", "6.458476e-09 +- 9.987567e-07\n", "1.214570e-08 +- 7.978997e-07\n", "7.730588e-09 +- 9.916993e-07\n", "3.446094e-08 +- 4.399442e-07\n", "-1.374185e-09 +- 9.996067e-07\n", "-1.893595e-08 +- 7.980139e-07\n", "5.554131e-09 +- 9.987667e-07\n", "7.803209e-09 +- 6.510553e-07\n", "6.298535e-09 +- 9.921945e-07\n", "2.104681e-08 +- 3.682657e-07\n", "-1.411297e-09 +- 9.994464e-07\n", "-2.146185e-08 +- 7.440881e-07\n", "4.019056e-09 +- 9.987651e-07\n", "1.465235e-09 +- 6.133097e-07\n", "2.387020e-09 +- 9.923050e-07\n", "4.185623e-09 +- 4.795961e-07\n", "-1.388277e-11 +- 9.999822e-07\n", "-2.239831e-08 +- 8.034459e-07\n", "3.232348e-09 +- 9.984780e-07\n", "-1.455338e-09 +- 7.820590e-07\n", "-9.375975e-11 +- 9.914788e-07\n", "3.763155e-09 +- 3.942358e-07\n", "-4.071492e-10 +- 9.998782e-07\n", "-2.436976e-08 +- 8.031597e-07\n", "2.897597e-09 +- 9.984760e-07\n", "-3.218325e-09 +- 7.992497e-07\n", "-3.781698e-10 +- 9.916597e-07\n", "7.623974e-10 +- 3.087616e-07\n", "-2.338609e-09 +- 9.993996e-07\n", "-7.574927e-09 +- 6.905508e-07\n", "2.657741e-10 +- 9.984656e-07\n", "-1.192275e-09 +- 5.334549e-07\n", "-4.510073e-09 +- 9.916174e-07\n", "-1.343541e-09 +- 2.615139e-07\n", "-2.166542e-09 +- 9.993541e-07\n", "-1.336719e-08 +- 6.418124e-07\n", "-3.291408e-09 +- 9.982039e-07\n", "5.343071e-09 +- 4.083534e-07\n", "-1.175790e-08 +- 9.901089e-07\n", "-9.620433e-10 +- 2.534437e-07\n", "-2.274883e-09 +- 9.993339e-07\n", "-2.147449e-08 +- 6.187636e-07\n", "-7.193686e-09 +- 9.979414e-07\n", "7.540476e-10 +- 3.732011e-07\n", "-1.939050e-08 +- 9.889693e-07\n", "5.657651e-09 +- 2.312513e-07\n", "-2.005346e-09 +- 9.988901e-07\n", "-2.286892e-08 +- 5.904385e-07\n", "-1.143159e-08 +- 9.978499e-07\n", "1.501917e-11 +- 3.606066e-07\n", "-2.775964e-08 +- 9.887855e-07\n", "2.251270e-08 +- 2.425927e-07\n", "-5.725857e-10 +- 9.988201e-07\n", "-1.649504e-08 +- 5.746467e-07\n", "-1.444328e-08 +- 9.978759e-07\n", "7.108739e-09 +- 3.328192e-07\n", "-3.401390e-08 +- 9.887941e-07\n", "4.373919e-08 +- 2.730622e-07\n", "1.412912e-09 +- 9.991441e-07\n", "-1.382214e-08 +- 6.022956e-07\n", "-1.570288e-08 +- 9.978439e-07\n", "2.069879e-08 +- 3.786143e-07\n", "-3.531721e-08 +- 9.895995e-07\n", "4.876416e-08 +- 3.100244e-07\n", "2.319812e-09 +- 9.993013e-07\n", "-1.841492e-08 +- 6.444512e-07\n", "-1.447953e-08 +- 9.980885e-07\n", "1.468859e-08 +- 3.910121e-07\n", "-3.290080e-08 +- 9.907276e-07\n", "5.431999e-08 +- 3.330066e-07\n", "4.116414e-09 +- 9.992303e-07\n", "-2.116690e-08 +- 6.593197e-07\n", "-1.062257e-08 +- 9.987691e-07\n", "1.004670e-08 +- 4.686340e-07\n", "-2.673844e-08 +- 9.929863e-07\n", "5.849792e-08 +- 3.632220e-07\n", "6.122503e-09 +- 9.993760e-07\n", "-1.265059e-08 +- 7.685147e-07\n", "-3.696737e-09 +- 9.996033e-07\n", "7.936107e-09 +- 4.755692e-07\n", "-1.371412e-08 +- 9.968872e-07\n", "-4.065645e-09 +- 9.978191e-07\n", "1.371516e-09 +- 9.998764e-07\n", "5.224203e-09 +- 9.981683e-07\n", "-4.460457e-11 +- 9.999919e-07\n", "6.932553e-09 +- 9.965115e-07\n", "-1.380716e-09 +- 9.998809e-07\n", "Finished processing arc 1\n", "\n", "\n", "Corrected values +- uncertainty:\n", "2.828166e+05 +- 1.062132e+02\n", "-1.859956e+06 +- 6.343194e+01\n", "3.175741e+06 +- 4.082172e+01\n", "-3.377927e+03 +- 1.358279e-02\n", "-1.762640e+02 +- 5.054785e-02\n", "1.866705e+02 +- 8.430600e-02\n", "1.006517e+00 +- 1.749712e+00\n", "5.595074e-01 +- 1.363543e+00\n", "1.000095e+00 +- 2.000000e+00\n", "-3.694030e-10 +- 9.694454e-07\n", "4.876139e-12 +- 9.999992e-07\n", "-1.186776e-11 +- 9.994232e-07\n", "-4.380736e-11 +- 9.999127e-07\n", "-8.502429e-11 +- 9.984393e-07\n", "-1.131535e-10 +- 9.995547e-07\n", "-6.430754e-10 +- 8.796636e-07\n", "4.854814e-12 +- 9.999992e-07\n", "-9.445853e-12 +- 9.993875e-07\n", "-4.610380e-11 +- 9.999049e-07\n", "-7.041238e-11 +- 9.982661e-07\n", "-1.132261e-10 +- 9.995544e-07\n", "-3.018956e-10 +- 7.042649e-07\n", "1.051480e-10 +- 9.999895e-07\n", "2.976226e-10 +- 9.992257e-07\n", "7.140716e-13 +- 9.998946e-07\n", "4.724835e-10 +- 9.978983e-07\n", "-2.551431e-11 +- 9.995558e-07\n", "2.111976e-08 +- 4.882927e-07\n", "-7.593063e-11 +- 9.999679e-07\n", "-1.613975e-08 +- 8.207200e-07\n", "2.861415e-10 +- 9.994899e-07\n", "6.698082e-09 +- 6.860901e-07\n", "1.004422e-09 +- 9.970075e-07\n", "3.028605e-08 +- 2.719944e-07\n", "2.185153e-10 +- 9.999175e-07\n", "-2.953208e-08 +- 4.739351e-07\n", "9.768104e-10 +- 9.980278e-07\n", "6.056302e-09 +- 6.183845e-07\n", "2.625589e-09 +- 9.916938e-07\n", "5.654131e-08 +- 3.021144e-07\n", "-5.152635e-10 +- 9.999259e-07\n", "-1.790506e-08 +- 4.684271e-07\n", "3.306712e-09 +- 9.971684e-07\n", "1.922937e-08 +- 6.344541e-07\n", "7.457434e-09 +- 9.890141e-07\n", "6.340199e-08 +- 3.608850e-07\n", "-3.756658e-10 +- 9.999314e-07\n", "-1.654472e-08 +- 5.364594e-07\n", "4.915522e-09 +- 9.968941e-07\n", "2.204885e-08 +- 7.469938e-07\n", "1.034528e-08 +- 9.883221e-07\n", "6.690558e-08 +- 4.888662e-07\n", "-6.353769e-10 +- 9.999433e-07\n", "-6.902186e-09 +- 8.203836e-07\n", "5.586824e-09 +- 9.968851e-07\n", "1.220262e-08 +- 8.145120e-07\n", "1.136235e-08 +- 9.867262e-07\n", "5.633371e-08 +- 7.443679e-07\n", "-4.912082e-10 +- 9.999763e-07\n", "-6.949635e-09 +- 8.209440e-07\n", "5.804441e-09 +- 9.966720e-07\n", "1.355121e-08 +- 8.249339e-07\n", "1.150660e-08 +- 9.865291e-07\n", "4.805081e-08 +- 4.843693e-07\n", "-6.412636e-10 +- 9.999615e-07\n", "-1.059950e-08 +- 7.161195e-07\n", "6.579752e-09 +- 9.965328e-07\n", "7.523378e-09 +- 7.754866e-07\n", "1.294614e-08 +- 9.876310e-07\n", "2.350848e-08 +- 2.401427e-07\n", "-4.328414e-10 +- 9.998655e-07\n", "-1.940701e-08 +- 3.708808e-07\n", "7.218393e-09 +- 9.958984e-07\n", "1.611833e-09 +- 5.721079e-07\n", "1.288076e-08 +- 9.847807e-07\n", "1.514294e-08 +- 2.151986e-07\n", "-4.185645e-10 +- 9.998527e-07\n", "-1.043633e-08 +- 3.392423e-07\n", "6.285730e-09 +- 9.958548e-07\n", "3.989354e-09 +- 5.745206e-07\n", "1.059324e-08 +- 9.845453e-07\n", "2.358573e-09 +- 2.165394e-07\n", "-3.064891e-10 +- 9.998461e-07\n", "-1.224608e-08 +- 3.376767e-07\n", "5.700212e-09 +- 9.964039e-07\n", "-3.520587e-09 +- 5.797520e-07\n", "8.956876e-09 +- 9.868157e-07\n", "-8.935772e-10 +- 2.322676e-07\n", "2.909251e-11 +- 9.998450e-07\n", "-1.630646e-08 +- 4.081786e-07\n", "4.686270e-09 +- 9.975295e-07\n", "-2.235139e-09 +- 6.309532e-07\n", "6.011218e-09 +- 9.904438e-07\n", "-2.898564e-09 +- 6.356505e-07\n", "2.971149e-10 +- 9.998964e-07\n", "-1.992785e-08 +- 8.440852e-07\n", "2.712244e-09 +- 9.984839e-07\n", "3.920779e-09 +- 8.864208e-07\n", "2.439817e-09 +- 9.916648e-07\n", "8.590854e-09 +- 8.081470e-07\n", "-8.605188e-11 +- 9.999849e-07\n", "-2.004486e-08 +- 9.033657e-07\n", "2.458757e-09 +- 9.982801e-07\n", "2.344909e-09 +- 8.960634e-07\n", "2.079035e-09 +- 9.913982e-07\n", "2.091422e-08 +- 8.838557e-07\n", "-8.434369e-11 +- 9.999862e-07\n", "-2.007744e-08 +- 9.033631e-07\n", "2.488002e-09 +- 9.981649e-07\n", "2.055358e-09 +- 8.956700e-07\n", "2.054093e-09 +- 9.914811e-07\n", "3.325455e-08 +- 7.988048e-07\n", "-7.805797e-11 +- 9.999867e-07\n", "-1.992653e-08 +- 9.037565e-07\n", "2.525615e-09 +- 9.980056e-07\n", "1.823881e-09 +- 8.943480e-07\n", "2.043870e-09 +- 9.914564e-07\n", "4.955496e-08 +- 6.731654e-07\n", "1.237388e-10 +- 9.999441e-07\n", "-2.243645e-08 +- 8.465010e-07\n", "2.389680e-09 +- 9.979317e-07\n", "9.150891e-09 +- 8.764649e-07\n", "1.195270e-09 +- 9.922820e-07\n", "5.844688e-08 +- 3.279304e-07\n", "4.105422e-10 +- 9.997688e-07\n", "-2.086723e-08 +- 4.417931e-07\n", "9.074881e-10 +- 9.977967e-07\n", "1.037424e-08 +- 6.939199e-07\n", "-1.468036e-09 +- 9.909101e-07\n", "6.747073e-08 +- 3.238944e-07\n", "1.220940e-10 +- 9.997399e-07\n", "-1.384415e-08 +- 4.103196e-07\n", "7.441299e-10 +- 9.977416e-07\n", "1.572136e-08 +- 6.773888e-07\n", "-2.000579e-09 +- 9.892917e-07\n", "6.490468e-08 +- 3.120760e-07\n", "1.442759e-10 +- 9.997121e-07\n", "-7.884293e-09 +- 4.064380e-07\n", "3.313324e-10 +- 9.977039e-07\n", "2.089988e-08 +- 6.713120e-07\n", "-3.012748e-09 +- 9.879681e-07\n", "4.878857e-08 +- 2.876840e-07\n", "4.747540e-10 +- 9.996675e-07\n", "-7.879915e-09 +- 4.130622e-07\n", "-1.102410e-10 +- 9.976887e-07\n", "9.402763e-09 +- 6.720671e-07\n", "-4.117159e-09 +- 9.876188e-07\n", "2.976581e-08 +- 2.639521e-07\n", "7.775925e-10 +- 9.996795e-07\n", "-1.824643e-08 +- 4.160312e-07\n", "-1.374804e-09 +- 9.976109e-07\n", "5.897483e-09 +- 6.731687e-07\n", "-6.300879e-09 +- 9.870208e-07\n", "1.830740e-08 +- 2.465006e-07\n", "1.786681e-10 +- 9.997662e-07\n", "-9.986439e-09 +- 4.112960e-07\n", "-1.973203e-09 +- 9.975494e-07\n", "7.123573e-09 +- 6.862036e-07\n", "-7.419497e-09 +- 9.877111e-07\n", "4.413686e-09 +- 2.433932e-07\n", "1.433249e-09 +- 9.996725e-07\n", "-1.548991e-08 +- 4.301186e-07\n", "-2.996963e-09 +- 9.980167e-07\n", "-2.272457e-09 +- 6.847508e-07\n", "-9.676214e-09 +- 9.906520e-07\n", "-5.502622e-09 +- 6.442826e-07\n", "7.409532e-10 +- 9.996882e-07\n", "-1.518189e-08 +- 8.493666e-07\n", "-4.997643e-09 +- 9.991563e-07\n", "9.198500e-09 +- 8.495964e-07\n", "-1.298823e-08 +- 9.944605e-07\n", "1.985287e-09 +- 7.916696e-07\n", "5.136528e-10 +- 9.999911e-07\n", "-1.718408e-08 +- 9.122684e-07\n", "-4.734746e-09 +- 9.992206e-07\n", "6.114636e-10 +- 9.066184e-07\n", "-1.220513e-08 +- 9.948852e-07\n", "1.075479e-08 +- 8.901805e-07\n", "4.991347e-10 +- 9.999916e-07\n", "-1.717608e-08 +- 9.123147e-07\n", "-4.979238e-09 +- 9.991404e-07\n", "3.480855e-11 +- 9.055220e-07\n", "-1.218340e-08 +- 9.949018e-07\n", "1.948758e-08 +- 8.820200e-07\n", "4.608817e-10 +- 9.999927e-07\n", "-1.715380e-08 +- 9.125406e-07\n", "-5.143097e-09 +- 9.990838e-07\n", "-1.029676e-10 +- 9.041718e-07\n", "-1.214835e-08 +- 9.949313e-07\n", "2.822389e-08 +- 7.641163e-07\n", "4.646963e-10 +- 9.999927e-07\n", "-1.707218e-08 +- 9.131881e-07\n", "-5.331451e-09 +- 9.990154e-07\n", "-1.884038e-10 +- 9.032590e-07\n", "-1.212627e-08 +- 9.949532e-07\n", "3.513394e-08 +- 7.031579e-07\n", "-1.605361e-09 +- 9.997593e-07\n", "-1.494768e-08 +- 9.326567e-07\n", "-4.104152e-09 +- 9.994481e-07\n", "-3.811563e-09 +- 8.590686e-07\n", "-9.037181e-09 +- 9.975657e-07\n", "-5.993235e-09 +- 9.980413e-07\n", "-1.163973e-09 +- 9.999249e-07\n", "-1.347288e-09 +- 9.999107e-07\n", "-2.097729e-10 +- 9.999978e-07\n", "-5.354376e-09 +- 9.984033e-07\n", "-1.043439e-09 +- 9.999384e-07\n", "Finished processing arc 4\n", "\n", "\n", "Corrected values +- uncertainty:\n", "4.980558e+05 +- 4.850891e+01\n", "-1.906087e+06 +- 5.180788e+01\n", "3.119866e+06 +- 3.165030e+01\n", "-3.351023e+03 +- 4.734807e-03\n", "-4.240505e+02 +- 2.365320e-02\n", "2.667209e+02 +- 3.607097e-02\n", "1.016748e+00 +- 1.724512e+00\n", "5.291431e-01 +- 1.311831e+00\n", "1.000053e+00 +- 2.000000e+00\n", "1.765516e-08 +- 4.834341e-07\n", "3.584700e-11 +- 9.999301e-07\n", "-1.361011e-08 +- 8.227553e-07\n", "1.591495e-09 +- 9.992413e-07\n", "1.519659e-10 +- 6.787975e-07\n", "4.518038e-09 +- 9.954101e-07\n", "6.650845e-09 +- 2.609190e-07\n", "-6.006387e-10 +- 9.998557e-07\n", "-1.107839e-08 +- 4.783194e-07\n", "4.090467e-09 +- 9.972667e-07\n", "-1.461837e-09 +- 6.128123e-07\n", "8.632524e-09 +- 9.872757e-07\n", "3.014491e-09 +- 2.600727e-07\n", "-3.732062e-10 +- 9.998493e-07\n", "-1.751337e-08 +- 4.737660e-07\n", "3.778205e-09 +- 9.963863e-07\n", "-2.520106e-09 +- 6.180612e-07\n", "7.601184e-09 +- 9.840318e-07\n", "4.577362e-09 +- 3.681297e-07\n", "-3.200554e-10 +- 9.999172e-07\n", "-2.333861e-08 +- 7.498572e-07\n", "3.932442e-09 +- 9.960693e-07\n", "-1.969152e-09 +- 7.948462e-07\n", "8.391101e-09 +- 9.824632e-07\n", "1.170331e-08 +- 6.157101e-07\n", "-2.947521e-10 +- 9.999538e-07\n", "-2.308260e-08 +- 7.478681e-07\n", "5.042358e-09 +- 9.954997e-07\n", "-5.602321e-09 +- 7.550817e-07\n", "9.893752e-09 +- 9.815307e-07\n", "1.807066e-08 +- 3.980378e-07\n", "-4.013254e-10 +- 9.999335e-07\n", "-1.946620e-08 +- 6.358624e-07\n", "4.327271e-09 +- 9.954559e-07\n", "-7.668616e-09 +- 6.942744e-07\n", "6.975122e-09 +- 9.825796e-07\n", "3.583481e-08 +- 2.778541e-07\n", "2.273411e-10 +- 9.998883e-07\n", "-2.545981e-08 +- 4.533197e-07\n", "1.389840e-09 +- 9.949401e-07\n", "8.903599e-09 +- 6.586137e-07\n", "9.338669e-10 +- 9.816859e-07\n", "5.397773e-08 +- 3.885346e-07\n", "-4.704855e-11 +- 9.998603e-07\n", "-1.392821e-08 +- 5.945494e-07\n", "3.601958e-10 +- 9.949237e-07\n", "1.980090e-08 +- 7.143344e-07\n", "-1.778745e-10 +- 9.823177e-07\n", "6.887317e-08 +- 5.936654e-07\n", "-3.185677e-11 +- 9.998687e-07\n", "-1.949171e-08 +- 5.050336e-07\n", "1.213760e-09 +- 9.949601e-07\n", "1.106007e-08 +- 6.741325e-07\n", "1.250970e-09 +- 9.808805e-07\n", "6.710535e-08 +- 4.101307e-07\n", "-4.410095e-10 +- 9.998854e-07\n", "-1.371121e-08 +- 5.650778e-07\n", "1.991598e-09 +- 9.953892e-07\n", "1.785446e-08 +- 7.006894e-07\n", "3.700734e-09 +- 9.829241e-07\n", "6.427818e-08 +- 3.192588e-07\n", "-7.797884e-10 +- 9.998122e-07\n", "-4.127361e-09 +- 4.237224e-07\n", "3.906100e-09 +- 9.957065e-07\n", "1.751548e-08 +- 6.633084e-07\n", "7.597528e-09 +- 9.829692e-07\n", "5.166618e-08 +- 3.337810e-07\n", "-9.068955e-10 +- 9.997984e-07\n", "-1.229635e-08 +- 5.369937e-07\n", "5.919431e-09 +- 9.959957e-07\n", "1.711420e-08 +- 7.308263e-07\n", "1.172250e-08 +- 9.839495e-07\n", "2.718406e-08 +- 4.006697e-07\n", "-4.651209e-10 +- 9.998257e-07\n", "-9.950726e-09 +- 6.228598e-07\n", "7.096356e-09 +- 9.962627e-07\n", "1.238609e-09 +- 7.217245e-07\n", "1.306446e-08 +- 9.832689e-07\n", "2.144330e-08 +- 2.921339e-07\n", "-4.135156e-10 +- 9.999046e-07\n", "-7.812864e-09 +- 5.693882e-07\n", "7.211793e-09 +- 9.960438e-07\n", "-2.958907e-10 +- 7.884924e-07\n", "1.219526e-08 +- 9.850031e-07\n", "4.572662e-09 +- 2.559938e-07\n", "-2.990561e-10 +- 9.997131e-07\n", "-1.510179e-08 +- 4.369433e-07\n", "6.213302e-09 +- 9.965738e-07\n", "3.644753e-09 +- 6.841363e-07\n", "9.898333e-09 +- 9.866495e-07\n", "-2.419941e-10 +- 5.237370e-07\n", "1.831720e-10 +- 9.997171e-07\n", "-2.179847e-08 +- 7.826788e-07\n", "5.431568e-09 +- 9.975348e-07\n", "-4.040409e-09 +- 8.375636e-07\n", "8.125834e-09 +- 9.872531e-07\n", "4.115578e-09 +- 7.910119e-07\n", "-3.257432e-10 +- 9.999706e-07\n", "-1.526265e-08 +- 8.432897e-07\n", "4.992098e-09 +- 9.972953e-07\n", "-5.793244e-10 +- 8.370889e-07\n", "7.174724e-09 +- 9.861700e-07\n", "2.284301e-09 +- 6.668819e-07\n", "-2.983086e-10 +- 9.999754e-07\n", "-1.528888e-08 +- 8.428041e-07\n", "5.123801e-09 +- 9.970675e-07\n", "-5.453438e-10 +- 8.300978e-07\n", "7.170008e-09 +- 9.861737e-07\n", "1.439847e-08 +- 4.585526e-07\n", "8.058508e-10 +- 9.996838e-07\n", "-1.727020e-08 +- 6.681677e-07\n", "4.174802e-09 +- 9.972425e-07\n", "-8.582599e-09 +- 7.829218e-07\n", "4.279294e-09 +- 9.876160e-07\n", "3.111536e-08 +- 2.656173e-07\n", "1.208274e-09 +- 9.996783e-07\n", "-2.244144e-08 +- 4.302210e-07\n", "1.308642e-09 +- 9.968907e-07\n", "3.040064e-09 +- 6.921165e-07\n", "-1.899072e-09 +- 9.855512e-07\n", "4.835832e-08 +- 4.249036e-07\n", "7.439381e-10 +- 9.994481e-07\n", "-1.882013e-08 +- 7.305237e-07\n", "-1.132422e-09 +- 9.971541e-07\n", "1.111564e-08 +- 7.777876e-07\n", "-6.322168e-09 +- 9.847462e-07\n", "6.443275e-08 +- 6.882250e-07\n", "2.833537e-10 +- 9.999694e-07\n", "-1.862914e-08 +- 7.362279e-07\n", "-1.445586e-09 +- 9.965462e-07\n", "6.656662e-09 +- 7.243745e-07\n", "-6.659694e-09 +- 9.824640e-07\n", "6.708638e-08 +- 4.539690e-07\n", "-2.883791e-10 +- 9.994727e-07\n", "-1.475607e-08 +- 5.928348e-07\n", "-9.329759e-10 +- 9.967120e-07\n", "1.771946e-08 +- 7.613528e-07\n", "-5.734386e-09 +- 9.834673e-07\n", "6.487307e-08 +- 3.182140e-07\n", "1.989908e-10 +- 9.993020e-07\n", "-9.384031e-09 +- 4.397213e-07\n", "-7.078558e-10 +- 9.965604e-07\n", "1.521591e-08 +- 6.741005e-07\n", "-5.540937e-09 +- 9.806863e-07\n", "4.996908e-08 +- 2.950759e-07\n", "4.891198e-10 +- 9.992504e-07\n", "-1.745343e-08 +- 4.144581e-07\n", "-8.638177e-10 +- 9.963498e-07\n", "9.676862e-09 +- 6.590596e-07\n", "-5.768873e-09 +- 9.792550e-07\n", "2.934348e-08 +- 2.656013e-07\n", "1.756271e-09 +- 9.990808e-07\n", "-1.037832e-08 +- 4.274842e-07\n", "-2.538853e-09 +- 9.964287e-07\n", "1.017044e-09 +- 6.636576e-07\n", "-9.102892e-09 +- 9.797899e-07\n", "1.934785e-08 +- 2.491676e-07\n", "1.132220e-09 +- 9.992302e-07\n", "-1.330095e-08 +- 4.648498e-07\n", "-4.626013e-09 +- 9.967028e-07\n", "3.855584e-09 +- 6.655232e-07\n", "-1.312455e-08 +- 9.823495e-07\n", "2.621702e-09 +- 5.392076e-07\n", "1.312337e-09 +- 9.992401e-07\n", "-1.541477e-08 +- 8.196635e-07\n", "-6.511413e-09 +- 9.979547e-07\n", "1.989182e-09 +- 7.932292e-07\n", "-1.615555e-08 +- 9.871044e-07\n", "6.747417e-09 +- 7.689758e-07\n", "6.800375e-10 +- 9.999765e-07\n", "-7.827237e-09 +- 8.753875e-07\n", "-6.532597e-09 +- 9.977930e-07\n", "7.061657e-10 +- 8.642264e-07\n", "-1.611870e-08 +- 9.866897e-07\n", "1.445502e-09 +- 8.032476e-07\n", "6.563913e-10 +- 9.999782e-07\n", "-7.877100e-09 +- 8.758774e-07\n", "-6.704636e-09 +- 9.976780e-07\n", "9.491050e-10 +- 8.589966e-07\n", "-1.623300e-08 +- 9.865037e-07\n", "7.448770e-09 +- 5.762359e-07\n", "-1.174141e-09 +- 9.997175e-07\n", "-1.437545e-08 +- 8.286314e-07\n", "-6.195976e-09 +- 9.978657e-07\n", "-7.230683e-09 +- 8.725949e-07\n", "-1.431027e-08 +- 9.892672e-07\n", "1.671608e-09 +- 5.932426e-07\n", "-2.667433e-09 +- 9.993245e-07\n", "3.971174e-09 +- 9.487127e-07\n", "-1.262135e-09 +- 9.997778e-07\n", "-6.685259e-09 +- 7.984869e-07\n", "-4.437210e-09 +- 9.978001e-07\n", "-3.292517e-10 +- 9.998257e-07\n", "-9.444617e-11 +- 9.999856e-07\n", "7.575369e-11 +- 9.999905e-07\n", "2.753878e-11 +- 9.999987e-07\n", "-3.029873e-10 +- 9.998527e-07\n", "-8.490557e-11 +- 9.999884e-07\n", "Finished processing arc 6\n" ] } ], "source": [ "# Process arcs in parallel using enumerate to get index\n", "with multiprocessing.get_context(\"fork\").Pool(num_processes) as pool:\n", " pool.map(process_arc, inputs)" ] }, { "cell_type": "markdown", "id": "ccbf64f9", "metadata": {}, "source": [ "## Results Analysis\n", "\n", "### Load Combined Results\n", "\n", "Aggregate results from all processed arcs for visualization and analysis." ] }, { "cell_type": "code", "execution_count": 7, "id": "838e85fb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing residuals from mro_outputs/arc_0\n", "Processing residuals from mro_outputs/arc_6\n", "Processing residuals from mro_outputs/arc_1\n", "Processing residuals from mro_outputs/arc_4\n", "Processing residuals from mro_outputs/arc_3\n", "Processing residuals from mro_outputs/arc_2\n", "Processing residuals from mro_outputs/arc_5\n" ] } ], "source": [ "# Get list of all arc directories\n", "arcDirs = [\n", " os.path.join(output_folder_base, d)\n", " for d in os.listdir(output_folder_base)\n", " if os.path.isdir(os.path.join(output_folder_base, d))\n", "]\n", "\n", "# Initialize lists to store combined data\n", "all_residuals = []\n", "all_arc_times = []\n", "\n", "# Load residuals from all arcs\n", "for arc_dir in arcDirs:\n", " arc_index = os.path.basename(arc_dir).split(\"_\")[-1]\n", " print(f\"Processing residuals from {arc_dir}\")\n", "\n", " try:\n", " # Load residuals\n", " residDf = pd.read_pickle(f\"{arc_dir}/residDf.pkl\")\n", " residDf[\"arc_index\"] = arc_index # Add identifier column\n", " all_residuals.append(residDf)\n", "\n", " # Load arc times\n", " arc_times = pickle.load(open(f\"{arc_dir}/arc_start_times.pkl\", \"rb\"))\n", " for time in arc_times:\n", " all_arc_times.append(time)\n", " except FileNotFoundError as e:\n", " print(f\"Warning: Could not load data from {arc_dir}: {e}\")" ] }, { "cell_type": "markdown", "id": "909324ce", "metadata": {}, "source": [ "### Plot 1: Doppler Residuals Over Time\n", "\n", "This figure shows three panels comparing different residual types:\n", "\n", "**Panel 1 - SPICE Residuals (w.r.t. SPK):**\n", "- Difference between observed Doppler and predicted values using SPICE ephemeris\n", "- Represents errors in the operational SPICE ephemeris\n", "- Typical RMS: ~10-20 mHz\n", "\n", "**Panel 2 - Prefit Residuals:**\n", "- Difference between observations and predicted values using SPICE state as initial guess\n", "- Shows residuals before orbit determination\n", "- Should be similar to SPICE residuals\n", "\n", "**Panel 3 - Postfit Residuals:**\n", "- Difference between observations and predicted values using estimated state\n", "- Shows residuals after orbit determination\n", "- Typical RMS: ~1-5 mHz (significant improvement over prefit)\n", "\n", "Lower postfit RMS indicates successful orbit determination with good fit to observations." ] }, { "cell_type": "code", "execution_count": 8, "id": "3e599a0f", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Combine all residual dataframes\n", "if all_residuals:\n", " combined_residDf = pd.concat(all_residuals, ignore_index=True)\n", " combined_residDf = combined_residDf.sort_values(by=\"time\")\n", " all_arc_times = np.array(sorted(all_arc_times))\n", "\n", " # Calculate overall RMS values\n", " prefitRMS = np.sqrt(np.mean(np.square(combined_residDf[\"prefit\"])))\n", " posfitRMS = np.sqrt(np.mean(np.square(combined_residDf[\"postfit\"])))\n", " spiceRMS = np.sqrt(np.mean(np.square(combined_residDf[\"spice\"])))\n", "\n", " # Plot residuals\n", " fig, axes = plt.subplots(3, 1, sharex=True, figsize=(20, 10))\n", "\n", " axes[0].set_title(f\"w.r.t. SPK, RMS = {spiceRMS*1e3:.2e} mHz\")\n", " axes[0].scatter(\n", " (combined_residDf[\"time\"] - combined_residDf[\"time\"].min()) / 86400,\n", " combined_residDf[\"spice\"],\n", " s=20,\n", " marker=\"o\",\n", " alpha=0.7,\n", " )\n", "\n", " axes[1].set_title(f\"Prefit RMS = {prefitRMS*1e3:.2e} mHz\")\n", " axes[1].scatter(\n", " (combined_residDf[\"time\"] - combined_residDf[\"time\"].min()) / 86400,\n", " combined_residDf[\"prefit\"],\n", " s=20,\n", " marker=\"o\",\n", " alpha=0.7,\n", " )\n", "\n", " axes[2].set_title(f\"Postfit RMS = {(posfitRMS*1e3):.2f} mHz\")\n", " axes[2].scatter(\n", " (combined_residDf[\"time\"] - combined_residDf[\"time\"].min()) / 86400,\n", " combined_residDf[\"postfit\"],\n", " s=20,\n", " marker=\"o\",\n", " alpha=0.7,\n", " )\n", "\n", " for ax in axes:\n", " ax.set_ylabel(\"Residuals [Hz]\")\n", " ax.grid(which=\"both\", linestyle=\"--\", linewidth=1.5)\n", "\n", " axes[0].set_ylim([-0.03, 0.03])\n", " axes[2].set_ylim([-0.03, 0.03])\n", "\n", " date_time_obj = time_representation.DateTime.from_epoch(\n", " time_representation.Time(combined_residDf[\"time\"].min())\n", " ).to_python_datetime()\n", " formatted_date = date_time_obj.strftime(\"%Y-%m-%d %H:%M:%S\")\n", " axes[2].set_xlabel(f\"Time [days since {formatted_date}]\")\n", "\n", "else:\n", " print(\"No residual data found!\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "6d7ec807", "metadata": {}, "source": [ "### Load State Difference Data\n", "\n", "Collect prefit and postfit orbit differences from SPICE ephemeris for all arcs." ] }, { "cell_type": "code", "execution_count": 9, "id": "8be05053", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing state differences from mro_outputs/arc_0\n", "Processing state differences from mro_outputs/arc_6\n", "Processing state differences from mro_outputs/arc_1\n", "Processing state differences from mro_outputs/arc_4\n", "Processing state differences from mro_outputs/arc_3\n", "Processing state differences from mro_outputs/arc_2\n", "Processing state differences from mro_outputs/arc_5\n" ] } ], "source": [ "# Collect all state difference data\n", "all_prefit_diff = []\n", "all_postfit_diff = []\n", "\n", "for arc_dir in arcDirs:\n", " arc_index = os.path.basename(arc_dir).split(\"_\")[1]\n", " print(f\"Processing state differences from {arc_dir}\")\n", "\n", " # Only process first 5 arcs for clearer visualization\n", " if int(arc_index) > 4:\n", " continue\n", "\n", " try:\n", " # Load prefit state differences\n", " prefit_df = pd.read_pickle(f\"{arc_dir}/rsw_state_difference_prefit.pkl\")\n", " prefit_df[\"arc_index\"] = arc_index\n", " all_prefit_diff.append((arc_index, prefit_df))\n", "\n", " # Load postfit state differences\n", " postfit_df = pd.read_pickle(f\"{arc_dir}/rsw_state_difference_postfit.pkl\")\n", " postfit_df[\"arc_index\"] = arc_index\n", " all_postfit_diff.append((arc_index, postfit_df))\n", " except FileNotFoundError as e:\n", " print(f\"Warning: Could not load state difference data from {arc_dir}: {e}\")" ] }, { "cell_type": "code", "execution_count": 10, "id": "1ad73471", "metadata": {}, "outputs": [], "source": [ "# Concatenate all postfit difference dataframes for RMS calculation\n", "if all_postfit_diff:\n", " # Extract just the dataframes from the (arc_index, df) tuples\n", " all_postfit_dfs = [df for _, df in all_postfit_diff]\n", "\n", " # Concatenate into a single dataframe\n", " combined_postfit_df = pd.concat(all_postfit_dfs, ignore_index=True)\n", "\n", " # Calculate overall RMS for each component\n", " components = [\"R\", \"T\", \"N\"]\n", " overall_rms = {}\n", "\n", " for component in components:\n", " overall_rms[component] = np.sqrt(\n", " np.mean(np.square(combined_postfit_df[component]))\n", " )" ] }, { "cell_type": "markdown", "id": "a7f6c349", "metadata": {}, "source": [ "### Plot 2: Orbit Differences from SPICE Ephemeris\n", "\n", "This figure shows orbit differences in the RSW (Radial-Along track-Cross track) frame:\n", "\n", "**Top Row - Prefit State Differences:**\n", "- Shows orbit differences before orbit determination\n", "- Each line represents a different arc (color-coded)\n", "- Vertical gray lines separate arc boundaries\n", "- Differences reflect errors in SPICE ephemeris\n", "\n", "**Bottom Row - Postfit State Differences:**\n", "- Shows orbit differences after orbit determination\n", "- RMS values shown in subplot titles\n", "\n", "**Interpretation:**\n", "- Postfit differences represent orbit determination accuracy relative to SPICE\n", "- Lower RMS indicates better agreement with SPICE ephemeris\n", "- Discontinuities at arc boundaries are expected (independent arc processing)\n", "- Overall RMS is computed across all arcs for each component" ] }, { "cell_type": "code", "execution_count": 11, "id": "d6f09805", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot state differences by arc (no concatenation)\n", "if all_prefit_diff and all_postfit_diff:\n", " # Sort by arc index\n", " all_prefit_diff.sort(key=lambda x: int(x[0]))\n", " all_postfit_diff.sort(key=lambda x: int(x[0]))\n", "\n", " # Find global min time for consistent x-axis\n", " global_t_min = min([df[\"t\"].min() for _, df in all_prefit_diff])\n", "\n", " # Create figure with 2x3 grid (R,T,N components for prefit and postfit)\n", " fig, axes = plt.subplots(2, 3, figsize=(20, 10), sharex=True)\n", "\n", " # Components to plot\n", " components = [\"R\", \"T\", \"N\"]\n", " component_colors = {\"R\": \"tab:blue\", \"T\": \"tab:orange\", \"N\": \"tab:green\"}\n", "\n", " # Plot prefit state differences by arc (first row)\n", " for i, (arc_index, df) in enumerate(all_prefit_diff):\n", " # Get relative time in days\n", " time_days = (df[\"t\"] - global_t_min) / 86400\n", "\n", " # Plot each component in its own panel\n", " for col, component in enumerate(components):\n", " axes[0, col].plot(\n", " time_days,\n", " df[component],\n", " \"o-\",\n", " label=f\"Arc {arc_index}\" if i == 0 else \"\",\n", " color=component_colors[component],\n", " alpha=0.7,\n", " markersize=3,\n", " )\n", "\n", " # Add light gray vertical lines to separate arcs\n", " if i < len(all_prefit_diff) - 1:\n", " arc_end = time_days.max()\n", " axes[0, col].axvline(\n", " arc_end, color=\"gray\", linestyle=\"-\", linewidth=0.5, alpha=0.3\n", " )\n", "\n", " # Plot postfit state differences by arc (second row)\n", " for i, (arc_index, df) in enumerate(all_postfit_diff):\n", " # Get relative time in days\n", " time_days = (df[\"t\"] - global_t_min) / 86400\n", "\n", " # Plot each component in its own panel\n", " for col, component in enumerate(components):\n", " axes[1, col].set_title(f\"Postfit RMS = {overall_rms[component]:.2f} m\")\n", " axes[1, col].plot(\n", " time_days,\n", " df[component],\n", " \"o-\",\n", " label=f\"Arc {arc_index}\" if i == 0 else \"\",\n", " color=component_colors[component],\n", " alpha=0.7,\n", " markersize=3,\n", " )\n", "\n", " # Add light gray vertical lines to separate arcs\n", " if i < len(all_postfit_diff) - 1:\n", " arc_end = time_days.max()\n", " axes[1, col].axvline(\n", " arc_end, color=\"gray\", linestyle=\"-\", linewidth=0.5, alpha=0.3\n", " )\n", "\n", " # Set titles and labels\n", " for col, component in enumerate(components):\n", " # Add y-axis labels\n", " axes[0, col].set_ylabel(f\"{component} Difference [m]\")\n", " axes[1, col].set_ylabel(f\"{component} Difference [m]\")\n", "\n", " # Add grid to all subplots\n", " axes[0, col].grid(which=\"both\", linestyle=\"--\", linewidth=1.5)\n", " axes[1, col].grid(which=\"both\", linestyle=\"--\", linewidth=1.5)\n", "\n", " # Add x-axis labels to bottom row\n", " for col in range(3):\n", " axes[1, col].set_xlabel(\"Time [days]\")\n", "\n", " plt.show()" ] }, { "cell_type": "markdown", "id": "6b120dcb", "metadata": {}, "source": [ "## Summary\n", "\n", "This notebook demonstrated multi-arc orbit determination for MRO using real DSN tracking data. Key results:\n", "\n", "**Physical Parameters Estimated:**\n", "- Initial state (position and velocity) for each arc\n", "- Solar radiation pressure scaling (~0.8-1.2)\n", "- Aerodynamic coefficients (drag and lift scaling)\n", "- Arc-wise empirical accelerations (per orbit, in along-track and cross-track)\n", "\n", "**Force Model Highlights:**\n", "- Mars gravity: 120x120 spherical harmonic expansion\n", "- Atmospheric drag: variable cross-section with macromodel\n", "- Solar/Mars radiation pressure: paneled target model\n", "- Third-body perturbations: Sun, Earth, Jupiter, Saturn, Phobos, Deimos\n", "- Tidal effects: Mars solid body tides from Sun and Phobos\n", "\n", "**Processing Notes:**\n", "- Total processing time: ~1-3 hours for all arcs (parallel execution)\n", "- Memory requirement: ~4-8 GB per arc\n", "- Output files: ~50-100 MB per arc\n", "\n", "For production orbit determination, consider:\n", "- Extending arc length (5-7 days) for better parameter correlation handling\n", "- Including range observations for improved radial accuracy\n", "- Estimating Mars gravity field parameters for geodesy applications\n", "- Using predicted attitude for more accurate radiation pressure modeling" ] } ], "metadata": { "kernelspec": { "display_name": "tudatpy", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.19" } }, "nbformat": 4, "nbformat_minor": 5 }