Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

L.M. Arthur, A.M. Decker, Cambridge, MA, 2025-2026

This code is a work in progress, and is not yet ready for use. It is being developed for research purposes in the MIT Laboratory for Nuclear Security and Policy.

Get started

pytrajlib is a library written in a combination of C and Python whose purpose is to simulate trajectories of missiles with ballistic and maneuvering reentry vehicles and determine their accuracy. Accuracy limits arise from errors that can be broadly categorized into guidance errors (estimates of the current missile state) or control (maneuverability) limitations. pytrajlib's key output is the distribution of miss distances. Samples of this distribution are obtained by Monte-Carlo sampling of error factors and trajectories.

Installation

pytrajlib is pip-installable, though we recommend using a packaging manager such as uv. To install, run

pip install pytrajlib

Quickstart

pytrajlib can be run as a command-line tool to facilitate use in larger scripts or other programming languages. For a list of commands, try

pytrajlib --help

To quickly get started, run

pytrajlib --num-runs 10 --num-processes 2

The primary outputs of interest are the impact data and the impact scatter plot, which shows the impact locations with downrange and crossrange components. These plots are created by default and saved by default to output/rv-maneuv/impact_plot.png along with detailed trajectory and reentry guidance information about the first run in CSV files.

To examine the first trajectory's characteristics, use the --plot-trajectory flag

pytrajlib --num-runs 10 --num-processes 2 --plot-trajectory

You can also use pytrajlib like any other Python package from a Python file or notebook:

import pytrajlib as ptl

impact_df = ptl.run(num_runs=10, num_processes=2, plot_trajectory=True)
print(impact_df)

Pytrajlib supports two primary reentry vehicle (RV) types: ballistic and maneuverable.

Maneuverable RVs are the default. A ballistic RV can be specified with the --rv-maneuv 0 flag:

# maneuverable (default)
pytrajlib --rv-maneuv 1


# ballistic without accurate positioning updates
pytrajlib --rv-maneuv 0 --gnss-nav 0

Detailed usage

All simulation and vehicle configs can be changed using a configuration .json file. Run the modified simulation with

pytrajlib --config path-to-your-config.json

The default configuration is

{
  # run parameters
  "run": {
    "run_name": "rv-maneuv", # Run identifier used for output folders and artifacts.
    "num_runs": 200, # Number of simulation runs to execute.
    "num_runs_optimizer": 50, # Number of Monte Carlo runs used by the boost and reentry optimizers.
    "num_trials_optimizer": 100, # Number of optimization trials per optimizer run.
    "time_step_boost": 0.001, # Time step used during the boost phase, in seconds.
    "time_step_lambert": 0.0001, # Time step used during Lambert maneuver, in seconds.
    "time_step_midcourse": 1.0, # Time step used during the midcourse phase, in seconds.
    "time_step_reentry": 0.0001, # Time step used during the reentry phase, in seconds.
    "traj_output": 1, # Write trajectory output logs for the first run with 1, and disable with 0.
    "range": 10000000, # Downrange distance in meters; supersedes the aimpoint.
    "x_aim": null, # Target aimpoint x-coordinate in meters.
    "y_aim": null, # Target aimpoint y-coordinate in meters.
    "z_aim": null, # Target aimpoint z-coordinate in meters.
    "integrator": 1, # Integrator selection; 0 is modified Euler-Maruyama, 1 is SRA3.
    "random_seed": -1, # Random seed used to initialize stochastic simulation inputs.
    "atm_path": null, # Path to the atmospheric profiles file used by the simulation.
    "optimize_boost": 0, # Optimize t_des_final and theta_long when set to 1.
    "optimize_reentry": 0 # Optimize reentry maneuver parameters (max_deflection_angle, gearing_ratio, nav_gain_0, nav_gain_1, K_q, K_pp, K_delta_p, K_delta_d) when set to 1.
  },
  # flight parameters
  "flight": {
    "grav_error": 1, # Enable the gravitational error model.
    "ballistic_drag": 0, # Use simplified drag; 1 enables it and 0 disables it.
    "atm_model": 2, # Atmospheric model selection; 0 is exponential, 1 adds perturbations, 2 is EarthGram, 3 is mean EarthGram.
    "gnss_nav": 1, # Enable GNSS position updates during exoatmospheric flight.
    "rv_maneuv": 1, # Reentry vehicle maneuverability mode; 1 uses realistic maneuverability, 2 uses idealized maneuverability.
    "perfect_boost": 0, # Set to 1 for a perfect boost phase and 0 for a realistic boost phase.
    "t_vert_boost": 10, # Vertical boost time, in seconds.
    "deflection_time": 0.02, # Actuator deflection time, in seconds.
    "actuator_force": 100, # Maximum actuator force in kN.
    "actuator_resolution": 0.01 # Actuator resolution in degrees.
  },
  # optimized parameters
  "optimized": {
    "theta_long": 0.6715065960788051, # Thrust angle from x axis in x-y plane.
    "theta_lat": 0.0, # Thrust angle above x-y plane.
    "t_des_final": 2986.6467267274857, # Desired final time for the boost phase, in seconds.
    "gearing_ratio": 18.902565748694002, # Actuator gearing ratio. Higher gearing ratios correspond to increased max force and decreased max speed.
    "max_deflection_angle": 5.005158800904358, # Maximum deflection angle allowed for the reentry vehicle in degrees.
    "nav_gain_0": 17.16392199274926, # Navigation gain at surface used by the reentry guidance law.
    "nav_gain_1": 1.8450079089106168, # Navigation gain at reentry used by the reentry guidance law.
    "K_q": 9.93839564932045, # Pitch-rate feedback gain.
    "K_pp": 12.011089458530302, # Proportional restoring angle of attack gain.
    "K_delta_p": 0.2397109503235244, # Proportional deflection gain.
    "K_delta_d": 12.81326407331284 # Derivative deflection gain.
  },
  # error parameters
  "error": {
    "initial_x_error": 0.0, # Initial x-position error.
    "initial_pos_error": 0.1, # Initial position error magnitude.
    "initial_vel_error": 0.001, # Initial velocity error magnitude.
    "initial_angle_error": 1e-06, # Initial angle error magnitude.
    "acc_scale_stability": 1e-06, # Accelerometer scale-factor stability.
    "gyro_bias_stability": 1e-08, # Gyroscope bias stability.
    "gyro_noise": 1e-08, # Gyroscope noise level.
    "gnss_noise": 0.1, # GNSS measurement noise level.
    "gnss_freq": 1.0, # GNSS update frequency in Hz.
    "roll_gyro_error_factor": 0.0, # Roll gyroscope error scaling factor.
    "burn_time_error": 0.1 # Burn time error magnitude in seconds.
  },
  # The vehicle parameters must be set in the json config. There is no command line support for modifying them.
  "vehicle": {
    # The default booster is based on the MMIII booster with three stages and a 
    # total burn time of 188 seconds.
    "booster": {
      "name": "MMIII",
      "area": 2.2698,
      "bus_mass": 100.0,
      "c_d_0": 0.15, # drag coefficient
      "stages": [
        {
          "wet_mass": 23230.0, # total mass of stage including fuel
          "fuel_mass": 20780.0,
          "isp0": 2619.27, # Isp * 9.81
          "burn_time": 61.0
        },
        {
          "wet_mass": 7270.0,
          "fuel_mass": 6240.0,
          "isp0": 2815.47,
          "burn_time": 66.0
        },
        {
          "wet_mass": 3710.0,
          "fuel_mass": 3306.0,
          "isp0": 2795.85,
          "burn_time": 61.0
        }
      ]
    },
    # The default reentry vehicle is based on SWERVE
    "rv": {
      "name": "SWERVE",
      "maneuverability_flag": 1,
      "rv_mass": 450.0,
      # Reference length is the tip to base length (m)
      "rv_length": 2.75,
      # Reference radius is the base radius (m)
      "rv_radius": 0.277,
      "half_angle": 0.0916, # Cone half angle in radians (5.5 degrees)
      "flap_area": 0.04, # flap area in square meters
      "x_flap": -2.65, # x-coordinate of the flap hinge in meters
      "x_com": -1.65, # x-coordinate of the center of mass in meters
      "Iyy": 290.0, # Moment of inertia around pitch/yaw axis
      "aerodynamics": {
        # If tabulated aerodynamic coefficients are present, then they will be used
        # to calculate the true state of the vehicle and the linear approximations will be 
        # used to calculate the guidance computer's estimated state of the vehicle.
        "c_d_0": 0.018, # Drag coefficient at zero angle of attack
        "c_d_alpha": 0.487, # Drag coefficient derivative per radian angle of attack
        "c_l_alpha": 1.988, # Lift coefficient derivative per radian angle of attack
        "c_m_alpha": -0.111, # Moment coefficient derivative per radian angle of attack
        "c_m_q": -0.429, # Moment coefficient derivative per radian/s angular velocity
        "c_m_delta": 0.059, # Moment coefficient derivative per radian flap deflection extent
        # Tabulated aerodynamic coefficients with angle of attack
        "alpha_deg_table": [
            0.0,0.2,0.4,0.6,0.8,1.0,1.2,1.4,1.6,1.8,2.0,2.2,2.4,2.6,2.8,3.0,3.2,3.4,3.6,3.8,4.0,4.2,4.4,4.6,4.8,5.0,5.2,5.4,5.6,5.8,6.0,6.2,6.4,6.6,6.8,7.0,7.2,7.4,7.6,7.8,8.0,8.2,8.4,8.6,8.8,9.0,9.2,9.4,9.6,9.8,10.0
        ],
        "c_d_table": [
            0.018,0.0181,0.0182,0.0183,0.0185,0.0188,0.0192,0.0196,0.0201,0.0205,0.0212,0.0219,0.0226,0.0234,0.0243,0.0252,0.0262,0.0273,0.0284,0.0296,0.0309,0.0322,0.0337,0.0351,0.0367,0.0383,0.0399,0.0417,0.0436,0.0455,0.0474,0.0494,0.0515,0.0538,0.0559,0.0583,0.0607,0.0632,0.0657,0.0684,0.0711,0.074,0.0769,0.0797,0.0828,0.0861,0.0893,0.0925,0.096,0.0994,0.1031
        ],
        "c_l_table": [
            0.0,0.006,0.012,0.018,0.024,0.03,0.036,0.042,0.048,0.055,0.061,0.067,0.073,0.08,0.086,0.092,0.099,0.105,0.112,0.118,0.125,0.131,0.138,0.144,0.151,0.157,0.164,0.171,0.177,0.184,0.191,0.198,0.205,0.212,0.219,0.226,0.234,0.241,0.249,0.256,0.264,0.272,0.28,0.288,0.296,0.304,0.313,0.321,0.329,0.338,0.347
        ],
        "c_m_table": [
            0.0,-0.0002,-0.0003,-0.0005,-0.0008,-0.001,-0.0012,-0.0014,-0.0016,-0.0018,-0.0021,-0.0023,-0.0026,-0.0029,-0.0032,-0.0035,-0.0038,-0.0041,-0.0043,-0.0047,-0.005,-0.0054,-0.0057,-0.0061,-0.0065,-0.0069,-0.0072,-0.0076,-0.0079,-0.0084,-0.0088,-0.0093,-0.0098,-0.0101,-0.0106,-0.011,-0.0115,-0.012,-0.0126,-0.013,-0.0135,-0.014,-0.0146,-0.0152,-0.0157,-0.0164,-0.0169,-0.0175,-0.0181,-0.0187,-0.0194
        ],
        "c_m_q_table": [
            -0.118,-0.119,-0.12,-0.121,-0.121,-0.122,-0.123,-0.124,-0.125,-0.125,-0.126,-0.127,-0.128,-0.129,-0.129,-0.13,-0.131,-0.131,-0.132,-0.133,-0.134,-0.135,-0.136,-0.136,-0.137,-0.138,-0.138,-0.14,-0.141,-0.143,-0.145,-0.147,-0.149,-0.151,-0.153,-0.155,-0.158,-0.16,-0.163,-0.165,-0.167,-0.17,-0.172,-0.175,-0.177,-0.18,-0.182,-0.185,-0.187,-0.19,-0.193
        ]
      }
    }
  }
}

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pytrajlib-1.0.0a47.tar.gz (274.4 kB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

pytrajlib-1.0.0a47-cp313-cp313-win_amd64.whl (328.1 kB view details)

Uploaded CPython 3.13Windows x86-64

pytrajlib-1.0.0a47-cp313-cp313-win32.whl (325.0 kB view details)

Uploaded CPython 3.13Windows x86

pytrajlib-1.0.0a47-cp313-cp313-musllinux_1_2_x86_64.whl (393.0 kB view details)

Uploaded CPython 3.13musllinux: musl 1.2+ x86-64

pytrajlib-1.0.0a47-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (393.3 kB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ x86-64manylinux: glibc 2.28+ x86-64

pytrajlib-1.0.0a47-cp313-cp313-macosx_11_0_arm64.whl (327.4 kB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

File details

Details for the file pytrajlib-1.0.0a47.tar.gz.

File metadata

  • Download URL: pytrajlib-1.0.0a47.tar.gz
  • Upload date:
  • Size: 274.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for pytrajlib-1.0.0a47.tar.gz
Algorithm Hash digest
SHA256 a02aa1c553be4d8eb0efb92504420fb6f2bd7c002e4f640138cb75d90bc8e946
MD5 8170befb44aa8ed0572862c96c686b2f
BLAKE2b-256 01ad591c30dd4758d03aa4174672b805742716954f43e98f4904c6f13203d992

See more details on using hashes here.

Provenance

The following attestation bundles were made for pytrajlib-1.0.0a47.tar.gz:

Publisher: build_wheels.yml on lmarthur/pytrajlib

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file pytrajlib-1.0.0a47-cp313-cp313-win_amd64.whl.

File metadata

File hashes

Hashes for pytrajlib-1.0.0a47-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 d2f58809bfe0d14be4d3759e90471d9d30a96bd0755b9bc5fe098cc24ae37479
MD5 ab9938da2c10e15d5250854f3b8a7081
BLAKE2b-256 a5de6574746d85e48398f7c2e523708d370852127e48fa4ca5d7d781b7c596cb

See more details on using hashes here.

Provenance

The following attestation bundles were made for pytrajlib-1.0.0a47-cp313-cp313-win_amd64.whl:

Publisher: build_wheels.yml on lmarthur/pytrajlib

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file pytrajlib-1.0.0a47-cp313-cp313-win32.whl.

File metadata

  • Download URL: pytrajlib-1.0.0a47-cp313-cp313-win32.whl
  • Upload date:
  • Size: 325.0 kB
  • Tags: CPython 3.13, Windows x86
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for pytrajlib-1.0.0a47-cp313-cp313-win32.whl
Algorithm Hash digest
SHA256 e7c958701bd60df3690d6391d5a2dabc0b63f5a9a977c6263f751a8f57cac778
MD5 4d868ab448cf881c6c515d956a1b130e
BLAKE2b-256 8dcbdc7752fd67ce89157082276f790d97d5ac37ead7e27fb403178479b9a51e

See more details on using hashes here.

Provenance

The following attestation bundles were made for pytrajlib-1.0.0a47-cp313-cp313-win32.whl:

Publisher: build_wheels.yml on lmarthur/pytrajlib

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file pytrajlib-1.0.0a47-cp313-cp313-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for pytrajlib-1.0.0a47-cp313-cp313-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 5d44109461b5b3e927318b586cbb9910f8d31130aea05ed0c4bcd8eb8a2bd425
MD5 25c7a7944187487bbbfb7987573bd081
BLAKE2b-256 4f699bfa3587d47eb62cb45201970de8dde13887def00016edc93056ee9397a5

See more details on using hashes here.

Provenance

The following attestation bundles were made for pytrajlib-1.0.0a47-cp313-cp313-musllinux_1_2_x86_64.whl:

Publisher: build_wheels.yml on lmarthur/pytrajlib

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file pytrajlib-1.0.0a47-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pytrajlib-1.0.0a47-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 41053c1d17a2b90be2fb3ec715ae5d687dbecc8042a5af0be8b605c5eaf91f65
MD5 c15eacb72be51993f7be94659c2eeb3c
BLAKE2b-256 77874b87ef6122b53a8e2bd422d261b2e8d7e4e634f383c071e22e60b3f03d5e

See more details on using hashes here.

Provenance

The following attestation bundles were made for pytrajlib-1.0.0a47-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl:

Publisher: build_wheels.yml on lmarthur/pytrajlib

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file pytrajlib-1.0.0a47-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pytrajlib-1.0.0a47-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 55ec324f26bc5b3f19b9e68f1e5212aecdf02273262908f9893f695840079c72
MD5 aa612f1936fec8d229aa75fcf03504a9
BLAKE2b-256 64f4075b505db72221d818ff5c09d5c1a5712e4a9fa8ae4d545f529f2e325953

See more details on using hashes here.

Provenance

The following attestation bundles were made for pytrajlib-1.0.0a47-cp313-cp313-macosx_11_0_arm64.whl:

Publisher: build_wheels.yml on lmarthur/pytrajlib

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

This release

1.0.0a47 This release

6 files

1.0.0a27

6 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page