Updating the documentation
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Petrônio Cândido
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docs/build/doctrees/environment.pickle
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docs/build/doctrees/environment.pickle
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@ -36,17 +36,18 @@ def generate_gaussian_linear(mu_ini, sigma_ini, mu_inc, sigma_inc, it=100, num=1
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def generate_linear_periodic_gaussian(period, mu_min, sigma_min, mu_max, sigma_max, it=100, num=10, vmin=None, vmax=None):
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"""
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Generates a periodic linear variation on mean and variance
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:param period:
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:param mu_min:
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:param sigma_min:
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:param mu_max:
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:param sigma_max:
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:param it:
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:param num:
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:param vmin:
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:param vmax:
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:return:
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:param period: the period of recurrence
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:param mu_min: initial (and minimum) mean of each period
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:param sigma_min: initial (and minimum) variance of each period
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:param mu_max: final (and maximum) mean of each period
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:param sigma_max: final (and maximum) variance of each period
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:param it: Number of iterations
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:param num: Number of samples generated on each iteration
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:param vmin: Lower bound value of generated data
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:param vmax: Upper bound value of generated data
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:return: A list of it*num float values
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"""
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if period > it:
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@ -78,21 +79,21 @@ def generate_linear_periodic_gaussian(period, mu_min, sigma_min, mu_max, sigma_m
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return ret
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def generate_senoidal_periodic_gaussian(period, mu_min, sigma_min, mu_max, sigma_max, it=100, num=10, vmin=None, vmax=None):
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def generate_sinoidal_periodic_gaussian(period, mu_min, sigma_min, mu_max, sigma_max, it=100, num=10, vmin=None, vmax=None):
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"""
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Generates a periodic sinoidal variation on mean and variance
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:param period:
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:param mu_min:
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:param sigma_min:
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:param mu_max:
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:param sigma_max:
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:param it:
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:param num:
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:param vmin:
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:param vmax:
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:return:
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:param period: the period of recurrence
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:param mu_min: initial (and minimum) mean of each period
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:param sigma_min: initial (and minimum) variance of each period
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:param mu_max: final (and maximum) mean of each period
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:param sigma_max: final (and maximum) variance of each period
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:param it: Number of iterations
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:param num: Number of samples generated on each iteration
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:param vmin: Lower bound value of generated data
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:param vmax: Upper bound value of generated data
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:return: A list of it*num float values
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"""
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mu_range = mu_max - mu_min
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sigma_range = sigma_max - sigma_min
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mu = mu_min
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@ -145,10 +146,21 @@ def generate_uniform_linear(min_ini, max_ini, min_inc, max_inc, it=100, num=10,
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def white_noise(n=500):
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"""
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Simple Gaussian noise signal
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:param n: number of samples
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:return:
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"""
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return np.random.normal(0, 1, n)
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def random_walk(n=500, type='gaussian'):
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"""
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Simple random walk
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:param n: number of samples
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:param type: 'gaussian' or 'uniform'
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:return:
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"""
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if type == 'gaussian':
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tmp = generate_gaussian_linear(0, 1, 0, 0, it=1, num=n)
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else:
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@ -185,6 +197,9 @@ def _append(additive, start, before, new):
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class SignalEmulator(object):
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"""
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Emulate a complex signal built from several additive and non-additive components
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"""
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def __init__(self, **kwargs):
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super(SignalEmulator, self).__init__()
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@ -196,14 +211,14 @@ class SignalEmulator(object):
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Creates a continuous Gaussian signal with mean mu and variance sigma.
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:param mu: mean
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:param sigma: variance
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:keyword cummulative: If False it cancels the previous signal and start this one, if True
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:keyword additive: If False it cancels the previous signal and start this one, if True
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this signal is added to the previous one
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:keyword start: lag index to start this signal, the default value is 0
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:keyword it: Number of iterations, the default value is 1
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:keyword length: Number of samples generated on each iteration, the default value is 100
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:keyword vmin: Lower bound value of generated data, the default value is None
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:keyword vmax: Upper bound value of generated data, the default value is None
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:return: A list of it*num float values
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:return: the current SignalEmulator instance, for method chaining
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"""
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parameters = {'mu': mu, 'sigma': sigma}
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self.components.append({'dist': 'gaussian', 'type': 'constant',
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@ -213,16 +228,14 @@ class SignalEmulator(object):
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def incremental_gaussian(self, mu, sigma, **kwargs):
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"""
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Creates an additive gaussian interference on a previous signal
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:param mu:
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:param sigma:
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:keyword cummulative: If False it cancels the previous signal and start this one, if True
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this signal is added to the previous one
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:param mu: increment on mean
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:param sigma: increment on variance
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:keyword start: lag index to start this signal, the default value is 0
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:keyword it: Number of iterations, the default value is 1
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:keyword length: Number of samples generated on each iteration, the default value is 100
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:keyword vmin: Lower bound value of generated data, the default value is None
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:keyword vmax: Upper bound value of generated data, the default value is None
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:return: A list of it*num float values
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:return: the current SignalEmulator instance, for method chaining
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"""
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parameters = {'mu': mu, 'sigma': sigma}
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self.components.append({'dist': 'gaussian', 'type': 'incremental',
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@ -232,16 +245,16 @@ class SignalEmulator(object):
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def periodic_gaussian(self, type, period, mu_min, sigma_min, mu_max, sigma_max, **kwargs):
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"""
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Creates an additive periodic gaussian interference on a previous signal
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:param mu:
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:param sigma:
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:keyword additive: If False it cancels the previous signal and start this one, if True
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this signal is added to the previous one
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:param type: 'linear' or 'sinoidal'
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:param period: the period of recurrence
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:param mu: increment on mean
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:param sigma: increment on variance
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:keyword start: lag index to start this signal, the default value is 0
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:keyword it: Number of iterations, the default value is 1
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:keyword length: Number of samples generated on each iteration, the default value is 100
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:keyword vmin: Lower bound value of generated data, the default value is None
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:keyword vmax: Upper bound value of generated data, the default value is None
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:return: A list of it*num float values
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:return: the current SignalEmulator instance, for method chaining
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"""
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parameters = {'type':type, 'period':period,
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'mu_min': mu_min, 'sigma_min': sigma_min, 'mu_max': mu_max, 'sigma_max': sigma_max}
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@ -251,10 +264,10 @@ class SignalEmulator(object):
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def blip(self, **kwargs):
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"""
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Creates an outlier greater than the maximum or lower then the minimum previous values of the signal,
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and insert it on a random location of the signal.
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:param intensity:
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:param kwargs:
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:return:
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:return: the current SignalEmulator instance, for method chaining
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"""
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parameters = {}
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self.components.append({'dist': 'blip', 'type': 'blip',
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@ -262,6 +275,11 @@ class SignalEmulator(object):
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return self
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def run(self):
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"""
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Render the signal
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:return: a list of float values
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"""
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signal = []
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last_it = 10
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last_num = 10
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@ -286,7 +304,7 @@ class SignalEmulator(object):
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mu_max, sigma_max = parameters['mu_max'],parameters['sigma_max']
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if parameters['type'] == 'sinoidal':
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tmp = generate_senoidal_periodic_gaussian(period, mu_min, sigma_min, mu_max, sigma_max,
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tmp = generate_sinoidal_periodic_gaussian(period, mu_min, sigma_min, mu_max, sigma_max,
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it=num, num=1, vmin=vmin, vmax=vmax)
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else:
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tmp = generate_linear_periodic_gaussian(period, mu_min, sigma_min, mu_max, sigma_max,
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