def simulate(self, *args):
"""
Simulate light curve generation using power spectrum or
impulse response.
Examples
--------
* x = simulate(beta):
For generating a light curve using power law spectrum.
Parameters:
* beta : float
Defines the shape of spectrum
* x = simulate(s):
For generating a light curve from user-provided spectrum.
**Note**: In this case, the `red_noise` parameter is provided.
You can generate a longer light curve by providing a higher
frequency resolution on the input power spectrum.
Parameters:
* s : array-like
power spectrum
* x = simulate(model):
For generating a light curve from pre-defined model
Parameters:
* model : astropy.modeling.Model
the pre-defined model
* x = simulate('model', params):
For generating a light curve from pre-defined model
Parameters:
* model : string
the pre-defined model
* params : list iterable or dict
the parameters for the pre-defined model
* x = simulate(s, h):
For generating a light curve using impulse response.
Parameters:
* s : array-like
Underlying variability signal
* h : array-like
Impulse response
* x = simulate(s, h, 'same'):
For generating a light curve of same length as input signal,
using impulse response.
Parameters:
* s : array-like
Underlying variability signal
* h : array-like
Impulse response
* mode : str
mode can be 'same', 'filtered, or 'full'.
'same' indicates that the length of output light
curve is same as that of input signal.
'filtered' means that length of output light curve
is len(s) - lag_delay
'full' indicates that the length of output light
curve is len(s) + len(h) -1
Parameters
----------
args
See examples below.
Returns
-------
lightCurve : `LightCurve` object
"""
if isinstance(args[0], (numbers.Integral, float)) and len(args) == 1:
return self._simulate_power_law(args[0])
elif isinstance(args[0], astropy.modeling.Model) and len(args) == 1:
return self._simulate_model(args[0])
elif utils.is_string(args[0]) and len(args) == 2:
return self._simulate_model_string(args[0], args[1])
elif len(args) == 1:
return self._simulate_power_spectrum(args[0])
elif len(args) == 2:
return self._simulate_impulse_response(args[0], args[1])
elif len(args) == 3:
return self._simulate_impulse_response(args[0], args[1], args[2])
else:
raise ValueError("Length of arguments must be 1, 2 or 3.")
def simulate(self, *args):
"""
Simulate light curve generation using power spectrum or
impulse response.
Examples
--------
* x = simulate(beta):
For generating a light curve using power law spectrum.
Parameters:
* beta : float
Defines the shape of spectrum
* x = simulate(s):
For generating a light curve from user-provided spectrum.
Parameters:
* s : array-like
power spectrum
* x = simulate(model):
For generating a light curve from pre-defined model
Parameters:
* model : astropy.modeling.Model
the pre-defined model
* x = simulate('model', params):
For generating a light curve from pre-defined model
Parameters:
* model : string
the pre-defined model
* params : list iterable or dict
the parameters for the pre-defined model
* x = simulate(s, h):
For generating a light curve using impulse response.
Parameters:
* s : array-like
Underlying variability signal
* h : array-like
Impulse response
* x = simulate(s, h, 'same'):
For generating a light curve of same length as input signal,
using impulse response.
Parameters:
* s : array-like
Underlying variability signal
* h : array-like
Impulse response
* mode : str
mode can be 'same', 'filtered, or 'full'.
'same' indicates that the length of output light
curve is same as that of input signal.
'filtered' means that length of output light curve
is len(s) - lag_delay
'full' indicates that the length of output light
curve is len(s) + len(h) -1
Parameters
----------
args
See examples below.
Returns
-------
lightCurve : `LightCurve` object
"""
if isinstance(args[0], (numbers.Integral, float)) and len(args) == 1:
return self._simulate_power_law(args[0])
elif isinstance(args[0], astropy.modeling.Model) and len(args) == 1:
return self._simulate_model(args[0])
elif utils.is_string(args[0]) and len(args) == 2:
return self._simulate_model_string(args[0], args[1])
elif len(args) == 1:
return self._simulate_power_spectrum(args[0])
elif len(args) == 2:
return self._simulate_impulse_response(args[0], args[1])
elif len(args) == 3:
return self._simulate_impulse_response(args[0], args[1], args[2])
else:
raise ValueError("Length of arguments must be 1, 2 or 3.")
def simulate(self, *args):
"""
Simulate light curve generation using power spectrum or
impulse response.
Examples
--------
- x = simulate(2)
For generating a light curve using power law spectrum.
Parameters
----------
Beta: int
Defines the shape of spectrum
N: int
Number of samples
Returns
-------
lightCurve: `LightCurve` object
- x = simulate(s)
For generating a light curve from user-provided spectrum.
Parameters
----------
s: array-like
power spectrum
Returns
-------
lightCurve: `LightCurve` object
- x = simulate('lorenzian', p)
For generating a light curve from pre-defined model
Parameters
----------
model: str
name of the pre-defined model
p: list iterable
model parameters. For details, see model definitions
in model.py
Returns
-------
lightCurve: `LightCurve` object
- x = simulate(s, h)
For generating a light curve using impulse response.
Parameters
----------
s: array-like
Underlying variability signal
h: array-like
Impulse response
Returns
-------
lightCurve: `LightCurve` object
- x = simulate(s, h, 'same')
For generating a light curve of same length as input
signal, using impulse response.
Parameters
----------
s: array-like
Underlying variability signal
h: array-like
Impulse response
mode: str
mode can be 'same', 'filtered, or 'full'.
'same' indicates that the length of output light
curve is same as that of input signal.
'filtered' means that length of output light curve
is len(s) - lag_delay
'full' indicates that the length of output light
curve is len(s) + len(h) -1
Returns
-------
lightCurve: `LightCurve` object
"""
if isinstance(args[0], numbers.Integral) and len(args) == 1:
return self._simulate_power_law(args[0])
elif len(args) == 1:
return self._simulate_power_spectrum(args[0])
elif utils.is_string(args[0]) and len(args) == 2:
return self._simulate_model(args[0], args[1])
elif len(args) == 2:
return self._simulate_impulse_response(args[0], args[1])
elif len(args) == 3:
return self._simulate_impulse_response(args[0], args[1], args[2])
else:
raise ValueError("Length of arguments must be 1, 2 or 3.")
def simulate(self, *args):
"""
Simulate light curve generation using power spectrum or
impulse response.
Examples
--------
- x = simulate(2)
For generating a light curve using power law spectrum.
Parameters
----------
Beta: int
Defines the shape of spectrum
N: int
Number of samples
Returns
-------
lightCurve: `LightCurve` object
- x = simulate(s)
For generating a light curve from user-provided spectrum.
Parameters
----------
s: array-like
power spectrum
Returns
-------
lightCurve: `LightCurve` object
- x = simulate('lorenzian', p)
For generating a light curve from pre-defined model
Parameters
----------
model: str
name of the pre-defined model
p: list iterable
model parameters. For details, see model definitions
in model.py
Returns
-------
lightCurve: `LightCurve` object
- x = simulate(s, h)
For generating a light curve using impulse response.
Parameters
----------
s: array-like
Underlying variability signal
h: array-like
Impulse response
Returns
-------
lightCurve: `LightCurve` object
- x = simulate(s, h, 'same')
For generating a light curve of same length as input
signal, using impulse response.
Parameters
----------
s: array-like
Underlying variability signal
h: array-like
Impulse response
mode: str
mode can be 'same', 'filtered, or 'full'.
'same' indicates that the length of output light
curve is same as that of input signal.
'filtered' means that length of output light curve
is len(s) - lag_delay
'full' indicates that the length of output light
curve is len(s) + len(h) -1
Returns
-------
lightCurve: `LightCurve` object
"""
if isinstance(args[0], numbers.Integral) and len(args) == 1:
return self._simulate_power_law(args[0])
elif len(args) == 1:
return self._simulate_power_spectrum(args[0])
elif utils.is_string(args[0]) and len(args) == 2:
return self._simulate_model(args[0], args[1])
elif len(args) == 2:
return self._simulate_impulse_response(args[0], args[1])
elif len(args) == 3:
return self._simulate_impulse_response(args[0], args[1], args[2])
else:
raise ValueError("Length of arguments must be 1, 2 or 3.")
