Welcome to the plumitas documentation!

plumitas

API

plumitas.core

plumitas.core.read_colvar(filename='COLVAR', multi=0, unbiased=False)

Function that takes experimental data and gives us the dependent/independent variables for analysis.

Parameters:
  • filename (string) – Name of the COLVAR file to read in.
  • multi (int) – Tells the method to read 1 or more COLVAR files. Default falsy value (0) means read only 1 file.
  • unbiased (bool) – If True, adds a ‘weight’ column of all 1s.
Returns:

df – CVs and bias as columns, time as index.
Return type:

Pandas DataFrame
plumitas.core.read_hills(filename='HILLS')

Function that takes experimental data and gives us the dependent/independent variables for analysis.

Parameters:filename (string) – Name of the COLVAR file to read in.
Returns:df – CVs and bias as columns, time as index.
Return type:Pandas DataFrame

plumitas.analysis

plumitas.analysis.get_frame_weights(df, temp, static_bias=None)

plumitas.visualization

plumitas.visualization.make_2d_free_energy_surface(df, x, y, temp, weight=None, bins=20, clim=None, xlim=None, ylim=None)

Create a 2D FES from a COLVAR file with static ‘pb.bias’. This function will be modularized and generalized, but I wanted to include something more exciting than reading colvar/hills files for the first PyPI cut.

Parameters:
  • df (Pandas DataFrame) – DataFrame generated from Plumed COLVAR file. This DataFrame must have a column with static ‘pb.bias’ - most likely generated from mdrun rerun - and at two CVs.
  • x (string) – Name of one of the CVs (column name from df).
  • y (string) – Name of one of the CVs (column name from df).
  • bins (int) – Number of bins in each dimension to segment histogram.
  • temp (float) – Temperature of simulation which generated Plumed file.
  • weight (str) – Name of static bias column.
  • clim (int) – Maximum free energy (in kJ/mol) for color bar.
  • xlim (tuple/list) – Limits for x axis in plot (i.e. [x_min, x_max]).
  • ylim (tuple/list) – Limits for y axis in plot (i.e. [y_min, y_max]).
Returns:

axes
Return type:

matplotlib.AxesSubplot
plumitas.visualization.potential_of_mean_force(df, collective_variables, temp, weight=None, bins=100, xlim=None, ylim=None)

Create PMF plot for one or several collective variables.

Parameters:
  • df (Pandas DataFrame) – DataFrame generated from Plumed COLVAR file. This DataFrame must have a column with static ‘pb.bias’ - most likely generated from mdrun rerun - and at two CVs.
  • collective_variables (list) – List of CVs you’d like to plot. These should be supplied in the form of a list of column names, or an instance of pd.Index using df.columns
  • temp (float) – Temperature of simulation which generated Plumed file.
  • weight (str) – Name of static bias column.
  • bins (int) – Number of bins in each dimension to segment histogram.
  • xlim (tuple/list) – Limits for x axis in plot (i.e. [x_min, x_max]).
  • ylim (tuple/list) – Limits for y axis in plot (i.e. [y_min, y_max]).
Returns:

axes
Return type:

matplotlib.AxesSubplot

Testing

plumitas.tests

plumitas.tests.test_plumitas
plumitas.tests.test_plumitas.test_make_2d_free_energy_surface()

Testing functions to reweight based on COLVAR from biased simulations and generate 2D free energy surface.

plumitas.tests.test_plumitas.test_potential_of_mean_force()

Testing functions to reweight based on COLVAR from biased simulations and generate 1D potential of mean force.

plumitas.tests.test_plumitas.test_read_files()

Testing function to convert of COLVAR file to pandas DataFrame.

Indices and tables