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-1.0.0a36.tar.gz (270.6 kB view details)

Uploaded Source

Built Distributions

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

pytrajlib-1.0.0a36-cp313-cp313-win_amd64.whl (323.8 kB view details)

Uploaded CPython 3.13Windows x86-64

pytrajlib-1.0.0a36-cp313-cp313-win32.whl (320.9 kB view details)

Uploaded CPython 3.13Windows x86

pytrajlib-1.0.0a36-cp313-cp313-musllinux_1_2_x86_64.whl (391.5 kB view details)

Uploaded CPython 3.13musllinux: musl 1.2+ x86-64

pytrajlib-1.0.0a36-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (391.8 kB view details)

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

pytrajlib-1.0.0a36-cp313-cp313-macosx_11_0_arm64.whl (323.7 kB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

File details

Details for the file pytrajlib-1.0.0a36.tar.gz.

File metadata

  • Download URL: pytrajlib-1.0.0a36.tar.gz
  • Upload date:
  • Size: 270.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for pytrajlib-1.0.0a36.tar.gz
Algorithm Hash digest
SHA256 551c81d33f336cda768673e823b7b3f0354be701bb62d0e821840571ff7e18e3
MD5 d984a6d5cb2d53156f95d1c5b1d92a80
BLAKE2b-256 373ab48a1b2d24db3e8dab5a3041caae1f30ab770b3973858319a3dbf040b02a

See more details on using hashes here.

Provenance

The following attestation bundles were made for pytrajlib-1.0.0a36.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-1.0.0a36-cp313-cp313-win_amd64.whl.

File metadata

File hashes

Hashes for pytrajlib-1.0.0a36-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 3c56d0ce3f2fd00204281eb7c156204d4bc146aa8bc89831a21bb862cb3e0b69
MD5 0424dbe626199ab39346510d94dfd57f
BLAKE2b-256 54b6161ea1aaf8c4b0a6ba93a2f4cfebeaedeb2b68739c594f6f2a260a195b8c

See more details on using hashes here.

Provenance

The following attestation bundles were made for pytrajlib-1.0.0a36-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-1.0.0a36-cp313-cp313-win32.whl.

File metadata

  • Download URL: pytrajlib-1.0.0a36-cp313-cp313-win32.whl
  • Upload date:
  • Size: 320.9 kB
  • Tags: CPython 3.13, Windows x86
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for pytrajlib-1.0.0a36-cp313-cp313-win32.whl
Algorithm Hash digest
SHA256 2e32720a358f0f44eb14f2e27c4fcfd344f8172dbb0b674bad8d11bd651fd665
MD5 d8add4b899be5235628033d3826aab4d
BLAKE2b-256 585f73eab2d4e084815c2e22c35416ff34e1ef0363da836d05efa43133ca34e5

See more details on using hashes here.

Provenance

The following attestation bundles were made for pytrajlib-1.0.0a36-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-1.0.0a36-cp313-cp313-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for pytrajlib-1.0.0a36-cp313-cp313-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 19ca7398c79de0206b36df0dd60eca8bfdef5827d9c23414780abf62d76dbc6f
MD5 63c9af3663174bbfed67b1a7633cf6f3
BLAKE2b-256 89bc74d49cada686bcde4ee53e07f16bf7677a523c65cef08d8ad65492d1fc0b

See more details on using hashes here.

Provenance

The following attestation bundles were made for pytrajlib-1.0.0a36-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-1.0.0a36-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pytrajlib-1.0.0a36-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 46e9ae92c421b74a9892d7e7c4186b579e3d22feeb0059be97bc7042644b650a
MD5 b50c9b5f87b9e5ffb049015b10880651
BLAKE2b-256 5cfbc27af53cd525673d1bc3bbab9a53f2918cebd716a2d9ec9e8e57676bdd5d

See more details on using hashes here.

Provenance

The following attestation bundles were made for pytrajlib-1.0.0a36-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-1.0.0a36-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pytrajlib-1.0.0a36-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 e19e27d50fe270431173dde74e21a90eacd1019fac9a641d8f242fd6a81f44c4
MD5 730d9e40e6d34629145987a2cd781ccb
BLAKE2b-256 c81611c98ec1fadbf27f472d92f367f9bc3b820a922c40ad05fc54095cb84f8c

See more details on using hashes here.

Provenance

The following attestation bundles were made for pytrajlib-1.0.0a36-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

1.0.0a36 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