def Mpost2R(mass, unit='Earth', classify='No'):
"""
Forecast the Radius distribution given the mass distribution.
Parameters
---------------
mass: one dimensional array
The mass distribution.
unit: string (optional)
Unit of the mass.
Options are 'Earth' and 'Jupiter'. Default is 'Earth'.
classify: string (optional)
If you want the object to be classifed.
Options are 'Yes' and 'No'. Default is 'No'.
Result will be printed, not returned.
Returns
---------------
radius: one dimensional array
Predicted radius distribution in the input unit.
"""
# mass input
mass = np.array(mass)
assert len(mass.shape) == 1, "Input mass must be 1-D."
# unit input
if unit == 'Earth':
pass
elif unit == 'Jupiter':
mass = mass * mearth2mjup
else:
print(
"Input unit must be 'Earth' or 'Jupiter'. Using 'Earth' as default."
)
# mass range
if np.min(mass) < 3e-4 or np.max(mass) > 3e5:
print('Mass range out of model expectation. Returning None.')
return None
## convert to radius
sample_size = len(mass)
logm = np.log10(mass)
prob = np.random.random(sample_size)
logr = np.ones_like(logm)
hyper_ind = np.random.randint(low=0,
high=np.shape(all_hyper)[0],
size=sample_size)
hyper = all_hyper[hyper_ind, :]
if classify == 'Yes':
classification(logm, hyper[:, -3:])
for i in range(sample_size):
logr[i] = piece_linear(hyper[i], logm[i], prob[i])
radius_sample = 10.**logr
## convert to right unit
if unit == 'Jupiter':
radius = radius_sample / rearth2rjup
else:
radius = radius_sample
return radius
def Mpost2R(mass, unit='Earth', classify='No'):
"""
Forecast the Radius distribution given the mass distribution.
Parameters
---------------
mass: one dimensional array
The mass distribution.
unit: string (optional)
Unit of the mass.
Options are 'Earth' and 'Jupiter'. Default is 'Earth'.
classify: string (optional)
If you want the object to be classifed.
Options are 'Yes' and 'No'. Default is 'No'.
Result will be printed, not returned.
Returns
---------------
radius: one dimensional array
Predicted radius distribution in the input unit.
"""
# mass input
mass = np.array(mass)
assert len(mass.shape) == 1, "Input mass must be 1-D."
# unit input
if unit == 'Earth':
pass
elif unit == 'Jupiter':
mass = mass * mearth2mjup
else:
print("Input unit must be 'Earth' or 'Jupiter'. "
"Using 'Earth' as default.")
# mass range
if np.min(mass) < 3e-4 or np.max(mass) > 3e5:
print('Mass range out of model expectation. Returning None.')
return None
## convert to radius
sample_size = len(mass)
logm = np.log10(mass)
prob = np.random.random(sample_size)
logr = np.ones_like(logm)
hyper_ind = np.random.randint(low=0, high=np.shape(all_hyper)[0],
size=sample_size)
hyper = all_hyper[hyper_ind,:]
if classify == 'Yes':
classification(logm, hyper[:,-3:])
for i in range(sample_size):
logr[i] = piece_linear(hyper[i], logm[i], prob[i])
radius_sample = 10.** logr
## convert to right unit
if unit == 'Jupiter':
radius = radius_sample / rearth2rjup
else:
radius = radius_sample
return radius
def Rpost2M(radius, unit='Earth', grid_size=1e3, classify='No'):
"""
Forecast the mass distribution given the radius distribution.
Parameters
---------------
radius: one dimensional array
The radius distribution.
unit: string (optional)
Unit of the mass. Options are 'Earth' and 'Jupiter'.
grid_size: int (optional)
Number of grid in the mass axis when sampling mass from radius.
The more the better results, but slower process.
classify: string (optional)
If you want the object to be classifed.
Options are 'Yes' and 'No'. Default is 'No'.
Result will be printed, not returned.
Returns
---------------
mass: one dimensional array
Predicted mass distribution in the input unit.
"""
# unit
if unit == 'Earth':
pass
elif unit == 'Jupiter':
radius = radius * rearth2rjup
else:
print(
"Input unit must be 'Earth' or 'Jupiter'. Using 'Earth' as default."
)
# radius range
if np.min(radius) < 1e-1 or np.max(radius) > 1e2:
print('Radius range out of model expectation. Returning None.')
return None
# sample_grid
if grid_size < 10:
print('The sample grid is too sparse. Using 10 sample grid instead.')
grid_size = 10
## convert to mass
sample_size = len(radius)
logr = np.log10(radius)
logm = np.ones_like(logr)
hyper_ind = np.random.randint(low=0,
high=np.shape(all_hyper)[0],
size=sample_size)
hyper = all_hyper[hyper_ind, :]
logm_grid = np.linspace(-3.522, 5.477, grid_size)
for i in range(sample_size):
prob = ProbRGivenM(logr[i], logm_grid, hyper[i, :])
logm[i] = np.random.choice(logm_grid, size=1, p=prob)
mass_sample = 10.**logm
if classify == 'Yes':
classification(logm, hyper[:, -3:])
## convert to right unit
if unit == 'Jupiter':
mass = mass_sample / mearth2mjup
else:
mass = mass_sample
return mass
def Rpost2M(radius, unit='Earth', grid_size = 1e3, classify = 'No'):
"""
Forecast the mass distribution given the radius distribution.
Parameters
---------------
radius: one dimensional array
The radius distribution.
unit: string (optional)
Unit of the mass. Options are 'Earth' and 'Jupiter'.
grid_size: int (optional)
Number of grid in the mass axis when sampling mass from radius.
The more the better results, but slower process.
classify: string (optional)
If you want the object to be classifed.
Options are 'Yes' and 'No'. Default is 'No'.
Result will be printed, not returned.
Returns
---------------
mass: one dimensional array
Predicted mass distribution in the input unit.
"""
# unit
if unit == 'Earth':
pass
elif unit == 'Jupiter':
radius = radius * rearth2rjup
else:
print("Input unit must be 'Earth' or 'Jupiter'. "
"Using 'Earth' as default.")
# radius range
if np.min(radius) < 1e-1 or np.max(radius) > 1e2:
print('Radius range out of model expectation. Returning None.')
return None
# sample_grid
if grid_size < 10:
print('The sample grid is too sparse. Using 10 sample grid instead.')
grid_size = 10
## convert to mass
sample_size = len(radius)
logr = np.log10(radius)
logm = np.ones_like(logr)
hyper_ind = np.random.randint(low=0, high=np.shape(all_hyper)[0],
size=sample_size)
hyper = all_hyper[hyper_ind,:]
logm_grid = np.linspace(-3.522, 5.477, grid_size)
for i in range(sample_size):
prob = ProbRGivenM(logr[i], logm_grid, hyper[i,:])
logm[i] = np.random.choice(logm_grid, size=1, p = prob)
mass_sample = 10.** logm
if classify == 'Yes':
classification(logm, hyper[:,-3:])
## convert to right unit
if unit == 'Jupiter':
mass = mass_sample / mearth2mjup
else:
mass = mass_sample
return mass
