glasspy package

Subpackages

• glasspy.chemistry package
  • Submodules
  • glasspy.chemistry.convert module
    • mol_to_wt()
    • rescale_array()
    • to_array()
    • to_element_array()
    • wt_to_mol()
  • glasspy.chemistry.data module
  • glasspy.chemistry.featurizer module
    • physchem_featurizer()
  • glasspy.chemistry.types module
    • ChemArray
  • Module contents
    • ChemArray
    • mol_to_wt()
    • physchem_featurizer()
    • rescale_array()
    • to_array()
    • to_element_array()
    • wt_to_mol()
• glasspy.data package
  • Submodules
  • glasspy.data.load module
    • SciGlass
      • SciGlass.data
      • SciGlass.available_properties()
      • SciGlass.available_properties_metadata()
      • SciGlass.elements_from_compounds()
      • SciGlass.get_compounds()
      • SciGlass.get_elements()
      • SciGlass.get_properties()
      • SciGlass.remove_duplicate_composition()
      • SciGlass.remove_zero_sum_columns()
  • glasspy.data.translators module
  • Module contents
    • SciGlass
      • SciGlass.data
      • SciGlass.available_properties()
      • SciGlass.available_properties_metadata()
      • SciGlass.elements_from_compounds()
      • SciGlass.get_compounds()
      • SciGlass.get_elements()
      • SciGlass.get_properties()
      • SciGlass.remove_duplicate_composition()
      • SciGlass.remove_zero_sum_columns()
• 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
      • Domain.compound
      • Domain.element
    • MLP
      • MLP.configure_optimizers()
      • MLP.distance_from_training()
      • MLP.domain
      • 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.save_training()
      • MLP.training_step()
      • MLP.validation_step()
    • MTL
      • MTL.training_step()
      • MTL.validation_step()
    • Predict
      • Predict.MAE()
      • Predict.MSE()
      • Predict.MedAE()
      • Predict.PercAE()
      • Predict.R2()
      • Predict.RD()
      • Predict.RMSE()
      • Predict.RRMSE()
      • Predict.domain
      • Predict.get_test_dataset()
      • Predict.get_training_dataset()
      • Predict.is_within_domain()
      • Predict.predict()
  • glasspy.predict.models module
    • GlassNet
      • GlassNet.predict()
    • GlassNetMTMH
      • GlassNetMTMH.forward()
      • GlassNetMTMH.hparams
      • GlassNetMTMH.target_trans
      • GlassNetMTMH.targets
      • GlassNetMTMH.training_file
    • GlassNetMTMLP
      • GlassNetMTMLP.hparams
      • GlassNetMTMLP.target_trans
      • GlassNetMTMLP.targets
      • GlassNetMTMLP.training_file
    • GlassNetSTNN
      • GlassNetSTNN.forward()
      • GlassNetSTNN.hparams
      • GlassNetSTNN.training_step()
      • GlassNetSTNN.validation_step()
    • ViscNet
      • ViscNet.absolute_features
      • ViscNet.featurizer()
      • ViscNet.hparams
      • ViscNet.log_viscosity_fun()
      • ViscNet.parameters_range
      • ViscNet.state_dict_path
      • ViscNet.weighted_features
      • ViscNet.x_mean
      • ViscNet.x_std
    • ViscNetHuber
    • ViscNetVFT
      • ViscNetVFT.log_viscosity_fun()
  • Module contents
    • GlassNet
      • GlassNet.predict()
    • GlassNetMTMH
      • GlassNetMTMH.allow_zero_length_dataloader_with_multiple_devices
      • GlassNetMTMH.forward()
      • GlassNetMTMH.hparams
      • GlassNetMTMH.precision
      • GlassNetMTMH.prepare_data_per_node
      • GlassNetMTMH.target_trans
      • GlassNetMTMH.targets
      • GlassNetMTMH.training
      • GlassNetMTMH.training_file
    • GlassNetMTMLP
      • GlassNetMTMLP.allow_zero_length_dataloader_with_multiple_devices
      • GlassNetMTMLP.hparams
      • GlassNetMTMLP.precision
      • GlassNetMTMLP.prepare_data_per_node
      • GlassNetMTMLP.target_trans
      • GlassNetMTMLP.targets
      • GlassNetMTMLP.training
      • GlassNetMTMLP.training_file
    • GlassNetSTNN
      • GlassNetSTNN.allow_zero_length_dataloader_with_multiple_devices
      • GlassNetSTNN.forward()
      • GlassNetSTNN.hparams
      • GlassNetSTNN.precision
      • GlassNetSTNN.prepare_data_per_node
      • GlassNetSTNN.training
      • GlassNetSTNN.training_step()
      • GlassNetSTNN.validation_step()
    • ViscNet
      • ViscNet.absolute_features
      • ViscNet.allow_zero_length_dataloader_with_multiple_devices
      • ViscNet.featurizer()
      • ViscNet.hparams
      • ViscNet.log_viscosity_fun()
      • ViscNet.parameters_range
      • ViscNet.precision
      • ViscNet.prepare_data_per_node
      • ViscNet.state_dict_path
      • ViscNet.training
      • ViscNet.weighted_features
      • ViscNet.x_mean
      • ViscNet.x_std
    • ViscNetHuber
    • ViscNetVFT
      • ViscNetVFT.log_viscosity_fun()
• glasspy.viscosity package
  • Submodules
  • glasspy.viscosity.diffusion module
    • diff_coeff_eyring()
    • diff_coeff_stokeseinstein()
  • glasspy.viscosity.equilibrium module
    • ag()
    • am()
    • am_alt()
    • myega()
    • myega_alt()
    • vft()
    • vft_alt()
  • glasspy.viscosity.equilibrium_log module
    • ag()
    • am()
    • am_alt()
    • myega()
    • myega_alt()
    • vft()
    • vft_alt()
  • Module contents

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.

Module contents