glasspy package
Subpackages
- glasspy.chemistry package
- glasspy.data package
- glasspy.predict package
- Submodules
- glasspy.predict.base module
AE
AE.configure_optimizers()
AE.distance_from_training()
AE.domain
AE.forward()
AE.get_test_dataset()
AE.get_training_dataset()
AE.get_validation_dataset()
AE.is_within_domain()
AE.learning_curve_train
AE.learning_curve_val
AE.load_training()
AE.predict()
AE.save_training()
AE.training_epoch_end()
AE.training_step()
AE.validation_epoch_end()
AE.validation_step()
Domain
MLP
MLP.configure_optimizers()
MLP.distance_from_training()
MLP.domain
MLP.forward()
MLP.get_test_dataset()
MLP.get_training_dataset()
MLP.is_within_domain()
MLP.learning_curve_train
MLP.learning_curve_val
MLP.load_training()
MLP.on_train_epoch_end()
MLP.on_validation_epoch_end()
MLP.predict()
MLP.save_training()
MLP.test_step()
MLP.training_step()
MLP.validation_step()
MTL
Predict
- glasspy.predict.models module
- Module contents
- glasspy.viscosity package
Submodules
glasspy.support module
- glasspy.support.round_number_with_error(x, error)
Round numbers with respect of their errors.
- x:
Array-like object with numbers. These are the values that will be rounded.
Error
Array-like object with numbers. These are the errors that will guid the rounding of x.
- Returns:
Rounded values of x. .. error:: Error of x with 1 significant digit.
- text:
String array with the values of x with error represented in parenthesis.
- Return type:
x
- Raises:
AssertionError – Happens when you have a negative value in the error argument. Errors must be positive.
- glasspy.support.round_to_closest_base(x, base)
Round numbers to closest base number.
- Parameters:
x – Array-like object with numbers.
base – Float to which the numbers will be rounded.
- Returns:
Numpy array with the rounded numbers.
Note
Implementation by Alok Singhal found in https://stackoverflow.com/a/2272174.
- glasspy.support.round_to_significant_figure(x, n)
Round numbers to n sifnificant digits.
- Parameters:
x – Array-like object with numbers.
n – Integer representing the number of significant digits to round to. Must be the same shape as x.
- Returns:
Numpy array with the numbers rounded to the desired significant digits.
Note
Implementation by Scott Gigante found in https://stackoverflow.com/a/59888924.