misc

Miscellaneous classes for supporting the package.

class casex.misc.InitialSpeeds(initial_speed_x_mu, initial_speed_x_sigma, initial_speed_y_mu, initial_speed_y_sigma)[source]

Class for holding initial speeds for ballistic descent.

Parameters

  • initial_speed_x_mu (float) – [m/s] The mean value of the normal distribution for the initial horizontal speed.

  • initial_speed_x_sigma (float) – The standard deviation of the normal distribution for the initial horizontal speed.

  • initial_speed_y_mu (float) – [m/s] The mean value of the normal distribution for the initial vertical speed.

  • initial_speed_y_sigma (float) – The standard deviation of the normal distribution for the initial vertical speed.

initial_speed_x

[m/s] The initial horizontal speed.

Type

float

initial_speed_y

[m/s] The initial vertical speed.

Type

float

class casex.misc.NormalDistributionParameters(mu=0.0, sigma=1.0, wrapping_type=<Wrapping.NONE: 2>)[source]

Class for provide support for generating and using normal distributions.

Parameters

  • mu (float, optional) – Mean of the normal distribution (the default is 0).

  • sigma (float, optional) – Standard deviation of the normal distribution (the default is 1).

  • wrapping_type (enums.Wrapping, optional) – The wrapping type for mu. When set to EWrapping.PI2PI, mu is wrapped to the interval -\(\pi\) to \(\pi\) (the default is EWrapping.NONE).

input_set

The domain for the sampling (i.e. the input to the distribution, or the x axis values).

Type

float array

output_set

The value set for the sampling (i.e. the output of the distribution, or the y axis values).

Type

float array

mu

Mean of the normal distribution (the default is 0).

Type

float, optional

sigma

Standard deviation of the normal distribution (the default is 1).

Type

float, optional

wrapping_type

The wrapping type for mu. When set to EWrapping.PI2PI, mu is wrapped to the interval -\(\pi\) to \(\pi\) (the default is EWrapping.NONE).

Type

enums.Wrapping, optional

compute_sampling(times_sigma, num_of_samples)[source]

Computes a sampling of the normal distribution.

The normal distribution can be plotted using output set against input set. This method computes the input set as a linear function and the output set as the normal distribution from the input set. Both sets are parameters in the class.

Parameters

  • times_sigma (float) – This value is multiplied onto sigma and the results plus/minus is the interval for the sampling.

  • num_of_samples (int) – Number of samples in the sampling.

Returns

Return type

None