omnetpypy.utilities
A set of utility classes and functions that are used across the package.
- class omnetpypy.utilities.FutureMetric(name, vector, mean, median, std, var, min, max, count, percentiles, type, columns)
Bases:
tupleA type that describes the metrics to collect during simulations. The fields, apart from the name, are booleans indicating the statistics to collect for the metric. Such fields are:
vector: the complete list of samples of the metric
mean: the mean of the values
median: the median of the values
std: the standard deviation of the values
var: the variance of the values
min: the minimum value
max: the maximum value
count: the number of samples
percentiles: the percentiles of the values (1, 5, 25, 75, 95, 99)
- columns
Alias for field number 11
- count
Alias for field number 8
- max
Alias for field number 7
- mean
Alias for field number 2
- median
Alias for field number 3
- min
Alias for field number 6
- name
Alias for field number 0
- percentiles
Alias for field number 9
- std
Alias for field number 4
- type
Alias for field number 10
- var
Alias for field number 5
- vector
Alias for field number 1
- class omnetpypy.utilities.MultiRandom(seeds=44)[source]
Bases:
RandomThis class is a wrapper around the standard library
random.Randomclass, that allows to use multiple random number generators with different seeds.- Parameters:
- seedslist or int
A list of seeds for the random number generators. The length of the list determines the number of generators. If a single integer is provided, a single generator is created.
- betavariate(alpha, beta, generator=0)[source]
Return a random floating point number N from a beta distribution.
- Parameters:
- alphafloat
The first shape parameter of the distribution. Can be any positive number.
- betafloat
The second shape parameter of the distribution. Can be any positive number.
- generatorint, optional
The index of the generator to use. Defaults to 0.
- Returns:
- float
The next random floating point number.
See also
random.Random.betavariate()
- choice(seq, generator=0)[source]
Return a random element from the non-empty sequence
seq.- Parameters:
- seqiterable
A non-empty sequence.
- generatorint, optional
The index of the generator to use. Defaults to 0.
- Returns:
- object
A random element from the sequence.
See also
random.Random.choice()
- choices(sequence, weights=None, cum_weights=None, k=1, generator=0)[source]
Return a k sized list of elements chosen from the population with replacement.
- Parameters:
- sequenceiterable
A non-empty sequence.
- weightslist, optional
A list of weights to be used in the selection. If
None, the weights are assumed to be equal. The relative weights determine the probability of selecting each element.- cum_weightslist, optional
A list of cumulative weights to be used in the selection. If
None, the weights are assumed to be equal. The relative weights determine the probability of selecting each element.- kint, optional
The number of elements to choose. Defaults to 1.
- generatorint, optional
The index of the generator to use. Defaults to 0.
- Returns:
- list
A list of k elements chosen from the population.
See also
random.Random.choices()
- expovariate(lambd=1.0, generator=0)[source]
Return a random floating point number N from a normal (Gaussian) distribution.
- Parameters:
- lambdfloat
The rate of the exponential distribution.
- generatorint, optional
The index of the generator to use. Defaults to 0.
- Returns:
- float
The next random floating point number.
See also
random.Random.expovariate()
- gammavariate(alpha, beta, generator=0)[source]
Return a random floating point number N from a gamma distribution.
- Parameters:
- alphafloat
The shape of the distribution. Can be any positive number.
- betafloat
The scale of the distribution. Can be any positive number.
- generatorint, optional
The index of the generator to use. Defaults to 0.
- Returns:
- float
The next random floating point number.
See also
random.Random.gammavariate()
- gauss(mu=0.0, sigma=1.0, generator=0)[source]
Return a random floating point number N from a normal (Gaussian) distribution.
- Parameters:
- mufloat
The mean of the distribution.
- sigmafloat
The standard deviation of the distribution.
- generatorint, optional
The index of the generator to use. Defaults to 0.
- Returns:
- float
The next random floating point number.
See also
random.Random.gauss()
- geometric(p, size=None, generator=0)[source]
Return a random integer N from a geometric distribution.
This is the only method that uses the numpy random number generator (
numpy.random.Generator) instead of the standard library random number generator (random.Random).- Parameters:
- pfloat
The probability of success.
- sizeint or tuple of ints or None, optional
The shape of the output. If None, a single value is returned.
- generatorint, optional
The index of the generator to use. Defaults to 0.
See also
numpy.random.Generator.geometric()
- getstate(generator=0)[source]
Return the internal state of the random number generator.
- Parameters:
- generatorint, optional
The index of the generator to use. Defaults to 0.
See also
random.Random.getstate()
- lognormvariate(mu=0.0, sigma=1.0, generator=0)[source]
Return a random floating point number N from a lognormal distribution.
- Parameters:
- mufloat
The mean of the distribution.
- sigmafloat
The standard deviation of the distribution.
- generatorint, optional
The index of the generator to use. Defaults to 0.
- Returns:
- float
The next random floating point number.
See also
random.Random.lognormvariate()
- normalvariate(mu=0.0, sigma=1.0, generator=0)[source]
Return a random floating point number N from a normal (Gaussian) distribution.
- Parameters:
- mufloat
The mean of the distribution.
- sigmafloat
The standard deviation of the distribution.
- generatorint, optional
The index of the generator to use. Defaults to 0.
- Returns:
- float
The next random floating point number.
See also
random.Random.normalvariate()
- paretovariate(alpha, generator=0)[source]
Return a random floating point number N from a Pareto distribution.
- Parameters:
- alphafloat
The shape of the distribution. Can be any positive number.
- generatorint, optional
The index of the generator to use. Defaults to 0.
- Returns:
- float
The next random floating point number.
See also
random.Random.parentovariate()
- randint(a, b, generator=0)[source]
Return the next random integer N such that a <= N <= b.
- Parameters:
- aint
The lower bound of the random integer.
- bint
The upper bound of the random integer.
- generatorint, optional
The index of the generator to use. Defaults to 0.
- Returns:
- int
The next random integer.
See also
random.Random.randint()
- random(generator=0)[source]
Return the next random floating point number uniformly distributed in the range [0.0, 1.0).
- Parameters:
- generatorint, optional
The index of the generator to use. Defaults to 0.
- Returns:
- float
The next random floating point number.
See also
random.Random.random()
- sample(population, k, generator=0)[source]
Return a k length list of unique elements chosen from the population sequence or set.
- Parameters:
- populationiterable
A non-empty sequence or set.
- kint
The number of unique elements to choose.
- generatorint, optional
The index of the generator to use. Defaults to 0.
- Returns:
- list
A list of unique elements chosen from the population.
See also
random.Random.sample()
- setstate(state, generator=0)[source]
Set the internal state of the random number generator.
- Parameters:
- statetuple
The internal state of the random number generator.
- generatorint, optional
The index of the generator to use. Defaults to 0.
See also
random.Random.setstate()
- shuffle(x, generator=0)[source]
Shuffle the sequence x in place.
- Parameters:
- xiterable
The sequence to shuffle.
- generatorint, optional
The index of the generator to use. Defaults to 0.
See also
random.Random.shuffle()
- triangular(low=0.0, high=0.0, mode=None, generator=0)[source]
Return a random floating point number N such that low <= N <= high and with the specified mode between bounds.
- Parameters:
- lowfloat
The lower bound of the random number.
- highfloat
The upper bound of the random number.
- modefloat or None, optional
The mode of the distribution. If None, the mode is the midpoint between the bounds.
- generatorint, optional
The index of the generator to use. Defaults to 0.
See also
random.Random.triangular()
- uniform(a, b, generator=0)[source]
Return a random floating point number N such that a <= N <= b for a <= b and b <= N <= a for b < a.
- Parameters:
- afloat
The lower bound of the random number.
- bfloat
The upper bound of the random number.
- generatorint, optional
The index of the generator to use. Defaults to 0.
- Returns:
- float
The next random floating point number.
See also
random.Random.uniform()
- vonmisesvariate(mu, kappa, generator=0)[source]
Return a random floating point number N from a von Mises distribution.
- Parameters:
- mufloat
The mean of the distribution.
- kappafloat
The concentration of the distribution.
- generatorint, optional
The index of the generator to use. Defaults to 0.
- Returns:
- float
The next random floating point number.
See also
random.Random.vonmisesvariate()
- weibullvariate(alpha, beta, generator=0)[source]
Return a random floating point number N from a Weibull distribution.
- Parameters:
- alphafloat
The shape of the distribution. Can be any positive number.
- betafloat
The scale of the distribution. Can be any positive number.
- generatorint, optional
The index of the generator to use. Defaults to 0.
- Returns:
- float
The next random floating point number.
See also
random.Random.weibullvariate()
- omnetpypy.utilities.get_metrics(metric, df)[source]
Compute the statistics for a metric and return them as a dictionary. Not used in the current version of the package because it requires all samples to be loaded in memory.
- Parameters:
- metric
FutureMetric The metric to collect.
- df
pandas.DataFrame The sampled data of the metric.
- metric
- Returns:
- dict
A dictionary containing the requested statistics for the metric.
- omnetpypy.utilities.get_metrics_from_csv(metric, filename)[source]
Compute the statistics for a metric and return them as a dictionary.
- Parameters:
- metric
FutureMetric The metric to collect.
- filenamestring
The name of the file containing the sampled data of the metric.
- metric
- Returns:
- dict
A dictionary containing the requested statistics for the metric.
- omnetpypy.utilities.time_unit_factor(unit)[source]
Return the factor to convert a time unit to seconds. For example, if the unit is “ms”, the factor is 1e-3.
- Parameters:
- unitstr
The time unit to convert. Can be one of the following:
“s” for seconds
“ms” for milliseconds
“us” for microseconds
“ns” for nanoseconds