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        "%matplotlib inline"
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      "source": [
        "\nExample performing a sensitivity analysis on a new model. \n"
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        "# Deriv prog model was selected because the model can be described as x' = x + dx*dt\nfrom prog_models.models.thrown_object import ThrownObject\nimport numpy as np\n\ndef run_example():\n    # Demo model\n    # Step 1: Create instance of model\n    m = ThrownObject()\n\n    # Step 2: Setup for simulation \n    def future_load(t, x=None):\n        return m.InputContainer({})\n\n    # Step 3: Setup range on parameters considered\n    thrower_height_range = np.arange(1.2, 2.1, 0.1)\n\n    # Step 4: Sim for each \n    event = 'impact'\n    eods = np.empty(len(thrower_height_range))\n    for (i, thrower_height) in zip(range(len(thrower_height_range)), thrower_height_range):\n        m.parameters['thrower_height'] = thrower_height\n        simulated_results = m.simulate_to_threshold(future_load, threshold_keys=[event], dt =1e-3, save_freq =10)\n        eods[i] = simulated_results.times[-1]\n\n    # Step 5: Analysis\n    print('For a reasonable range of heights, impact time is between {} and {}'.format(round(eods[0],3), round(eods[-1],3)))\n    sensitivity = (eods[-1]-eods[0])/(thrower_height_range[-1] - thrower_height_range[0])\n    print('  - Average sensitivity: {} s per cm height'.format(round(sensitivity/100, 6)))\n    print(\"  - It seems impact time is not very sensitive to thrower's height\")\n\n    # Now lets repeat for throw speed\n    throw_speed_range = np.arange(20, 40, 1)\n    eods = np.empty(len(throw_speed_range))\n    for (i, throw_speed) in zip(range(len(throw_speed_range)), throw_speed_range):\n        m.parameters['throwing_speed'] = throw_speed\n        simulated_results = m.simulate_to_threshold(future_load, threshold_keys=[event], options={'dt':1e-3, 'save_freq':10})\n        eods[i] = simulated_results.times[-1]\n\n    print('\\nFor a reasonable range of throwing speeds, impact time is between {} and {}'.format(round(eods[0],3), round(eods[-1],3)))\n    sensitivity = (eods[-1]-eods[0])/(throw_speed_range[-1] - throw_speed_range[0])\n    print('  - Average sensitivity: {} s per m/s speed'.format(round(sensitivity/100, 6)))\n    print(\"  - It seems impact time is much more dependent on throwing speed\")\n\n# This allows the module to be executed directly \nif __name__=='__main__':\n    run_example()"
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