SST2.analysis package

Submodules

SST2.analysis.data_plot module

SST2.analysis.data_plot.compare_weight_RMSD(df, x='$Time\\;(\\mu s)$', hue='sim', ener='new_pot', time_ax_name='$Time\\;(\\mu s)$', max_data=50000)[source]

SST2.analysis.data_plot.compute_Tm(temperatures, folding_fraction)[source]

Compute the melting temperature using a sigmoidal curve fit.

Parameters:

temperatureslist: List of temperatures.
folding_fractionlist: List of folding fraction.

Returns:

Tmfloat: Melting temperature.

SST2.analysis.data_plot.compute_cluster_hdbscan(pca_df, min_cluster_size=50, min_samples=50)[source]

Cluster the data using the HDBSCAN algorithm.

Parameters:

pca_dfpandas.DataFrame: Dataframe with the data to cluster.
min_cluster_sizeint, optional: Minimum cluster size. The default is 50.
min_samplesint, optional: Minimum number of samples. The default is 50.

Returns:

clust_seriepandas.Categorical: Categorical serie with the cluster.

SST2.analysis.data_plot.compute_cluster_kmean(pca_df, max_cluster=20, random_state=0)[source]

Cluster the data using the KMeans algorithm.

Parameters:

pca_dfpandas.DataFrame: Dataframe with the data to cluster.
max_clusterint, optional: Maximum number of cluster to test. The default is 20.
random_stateint, optional: Random state for the algorithm. The default is 0.

Returns:

clust_seriepandas.Categorical: Categorical serie with the cluster.
kmeans.cluster_centers_numpy.ndarray: Cluster centers.

SST2.analysis.data_plot.compute_exchange_prob(df, temp_col='Aim Temp (K)', time_ax_name='$Time\\;(\\mu s)$', exchange_time=2)[source]

Compute the exchange probability and the round trip time for a given dataframe. The dataframe should be the result of a SST2 simulation. The dataframe should have a column with the temperature and a column with the time.

Parameters:

dfpandas.DataFrame: Dataframe with the SST2 simulation data.
temp_colstr, optional: Name of the column with the temperature. The default is “Aim Temp (K)”.
time_ax_namestr, optional: Name of the column with the time. The default is r”$Time;(mu s)$”.
exchange_timefloat, optional: Time in ps between two exchange. The default is 2 ps.

Returns:

ex_probfloat: Exchange probability.
trip_timefloat: Round trip time in ns.

SST2.analysis.data_plot.compute_folding_fraction(df, col='RMSD (nm)', cutoff=0.18, temp_col='Aim Temp (K)', temp_list=None)[source]

Compute the fraction of folded protein.

Parameters:

dfpandas.DataFrame: Dataframe with the data to plot.
colstr, optional: Column to compute folding fraction. The default is “RMSD (nm)”.
cutofffloat, optional: Cutoff value for the folding fraction. The default is 0.18 nm.
temp_colstr, optional: Column with the temperature. The default is ‘Aim Temp (K)’.
temp_listlist, optional: List of temperature to use. The default is None.

Returns:

fold_fraclist: List of folding fraction.

SST2.analysis.data_plot.compute_folding_fraction_RMSD(df, col='RMSD (nm)', temp_col='Aim Temp (K)', cutoff=0.18, start_time=0, time_ax_name='$Time\\;(\\mu s)$', ref_fold_frac=None, time_interval=2.0)[source]

SST2.analysis.data_plot.compute_moving_average(df, ener='new_pot', col_name='avg_ener')[source]

SST2.analysis.data_plot.compute_weight_RMSD(df, final_weight_dict=None, ener='new_pot')[source]

SST2.analysis.data_plot.count_clust_transition(df, dt=None, sim_name_col='sim', clust_col='clust', time_ax_name='$Time\\;(\\mu s)$')[source]

SST2.analysis.data_plot.count_rmsd_transition(df, rmsd_fold=0.2, rmsd_unfold=0.4, dt=None, sim_name_col='sim', rmsd_col='RMSD (nm)', time_ax_name='$Time\\;(\\mu s)$')[source]

SST2.analysis.data_plot.filter_df(df, max_point_number)[source]

Filter a dataframe to keep a maximum number of data point. The dataframe is filtered with a step size computed to keep the maximum number of data point.

Parameters:

dfpandas.DataFrame: Dataframe to filter.
max_point_numberint: Maximum number of data point to keep.

Returns:

local_dfpandas.DataFrame: Filtered dataframe.

SST2.analysis.data_plot.get_quant_min_max(pd_serie, quant=0.001)[source]

Get the min and max value of a pandas serie from a quantile.

Parameters:

pd_seriepandas.Series: Pandas serie to analyze.
quantfloat, optional: Quantile to use. The default is 0.001.

Returns:

val_minfloat: Minimum value.
val_maxfloat: Maximum value.

SST2.analysis.data_plot.plot_distri_norm(df, x, hue, x_label=None, max_data=50000, bins=100, element='step', quant=None, bw_adjust=None)[source]

Plot a distribution plot with a gaussian filter on the y axis.

Parameters:

dfpandas.DataFrame: Dataframe with the data to plot.
xstr: Name of the column with the x axis data.
huestr: Name of the column with the hue data.
x_labelstr, optional: Label of the x axis. The default is None.
max_dataint, optional: Maximum number of data point to plot. The default is 20000.
binsint, optional: Number of bins. The default is 100.
elementstr, optional: Element of the plot. The default is “step”.
quantfloat, optional: Quantile to use to filter the data. The default is None.
bw_adjustfloat, optional: Bandwidth adjustment for the kernel density estimate. The default is None.

Returns:

ax1matplotlib.axes._subplots.AxesSubplot: Axes of the plot.

SST2.analysis.data_plot.plot_energie_swap_convergence(df, ener_name='new_pot', lag_num=4, time_ax_name='$Time\\;(\\mu s)$', ylabel='$E_{p}$', split_graph=False, ci=95, avg_start=None)[source]

SST2.analysis.data_plot.plot_energie_swap_convergence_diff(df, ener_name='new_pot', lag_num=4, time_ax_name='$Time\\;(\\mu s)$', ylabel='$E_{p}$', hue=None, color=None, label='$T_{m-1}$ update to $T_{m}$', errorbar=('ci', 95), avg_start=None)[source]

SST2.analysis.data_plot.plot_energie_swap_distri_diff(df, lag_num_list, ener_name='new_pot', time_ax_name='$Time\\;(\\mu s)$', temp_index=1, ylabel='$E_{p}$', hue=None, bins=100, element='step', ci=95, avg_start=0)[source]

SST2.analysis.data_plot.plot_folding_fraction(df, col='RMSD (nm)', cutoff=0.18, label=None, start_time=0, time_ax_name='$Time\\;(\\mu s)$', recompute_temp_flag=True, temp_col='Aim Temp (K)', ref_temp=300.0)[source]

Plot the fraction of folded protein as a function of the temperature.

Parameters:

dfpandas.DataFrame: Dataframe with the data to plot.
colstr, optional: Column to compute folding fraction. The default is “RMSD (nm)”.
cutofffloat, optional: Cutoff value for the folding fraction. The default is 0.18 nm.
labelstr, optional: Label of the plot. The default is None.
start_timefloat, optional: Start time of the simulation. The default is 0 us.
time_ax_namestr, optional: Name of the time axis. The default is r”$Time;(mu s)$”.
recompute_temp_flagbool, optional: Recompute the temperature. The default is True.
temp_colstr, optional: Column with the temperature. The default is ‘Aim Temp (K)’.
ref_tempfloat, optional: Reference temperature. The default is 300.0.

Returns:

Tmfloat: Melting temperature

SST2.analysis.data_plot.plot_folding_fraction_RMSD(df, col='RMSD (nm)', cutoff=0.18, label=None, start_time=0, time_ax_name='$Time\\;(\\mu s)$', ref_fold_frac=None, color=None, ls='-', s=20, alpha=1.0, time_interval=2.0)[source]

SST2.analysis.data_plot.plot_folding_fraction_convergence(df, col='RMSD (nm)', cutoff=0.18, label=None, start_time=0, time_ax_name='$Time\\;(\\mu s)$', recompute_temp_flag=False, ref_temp=300.0, time_interval=2.0)[source]

SST2.analysis.data_plot.plot_free_energy(xall, yall, weights=None, ax=None, nbins=100, ncontours=100, avoid_zero_count=False, minener_zero=True, kT=2.479, vmin=None, vmax=None, cmap='nipy_spectral', cbar=True, cbar_label='free energy (kJ/mol)', cax=None, levels=None, cbar_orientation='vertical', norm=None, range=None, level_gap=None)[source]

Plot the free energy of a 2D histogram.

Adapted from; https://github.com/markovmodel/PyEMMA/blob/devel/pyemma/plots/plots2d.py

Parameters:

xallnp.array: Array with the x data.
yallnp.array: Array with the y data.
weightsnp.array, optional: Array with the weights. The default is None.
axmatplotlib.axes._subplots.AxesSubplot, optional: Axes of the plot. The default is None.
nbinsint, optional: Number of bins. The default is 100.
ncontoursint, optional: Number of contours. The default is 100.
avoid_zero_countbool, optional: Avoid zero count. The default is False.
minener_zerobool, optional: Minimum energy to zero. The default is True.
kTfloat, optional: kT value. The default is 2.479.
vminfloat, optional: Minimum value. The default is None.
vmaxfloat, optional: Maximum value. The default is None.
cmapstr, optional: Colormap. The default is ‘nipy_spectral’.
cbarbool, optional: Add colorbar. The default is True.
cbar_labelstr, optional: Label of the colorbar. The default is ‘free energy (kJ/mol)’.
caxmatplotlib.axes._subplots.AxesSubplot, optional: Axes of the colorbar. The default is None.
levelsint, optional: Number of levels. The default is None.
cbar_orientationstr, optional: Orientation of the colorbar. The default is ‘vertical’.
normmatplotlib.colors.Normalize, optional: Normalize object. The default is None.
rangelist, optional: Range of the data. The default is None.
level_gapfloat, optional: Gap between levels. The default is None.

Returns:

figmatplotlib.figure.Figure: Figure of the plot.
axmatplotlib.axes._subplots.AxesSubplot: Axes of the plot.
miscdict: Dictionary with the colorbar.

SST2.analysis.data_plot.plot_lineplot_avg(df, x, y, quant=None, color='black', max_data=50000, avg_win=1000)[source]

Plot a lineplot with a gaussian filter on the y axis.

Parameters:

dfpandas.DataFrame: Dataframe with the data to plot.
xstr: Name of the column with the x axis data.
ystr: Name of the column with the y axis data.
quantfloat, optional: Quantile to use to filter the data. The default is None.
colorstr, optional: Color of the line. The default is “black”.
max_dataint, optional: Maximum number of data point to plot. The default is 50000.
avg_winint, optional: Window size of the gaussian filter. The default is 1000.

Returns:

gmatplotlib.axes._subplots.AxesSubplot: Axes of the plot.

SST2.analysis.data_plot.plot_rung_occupancy(df, hue='group')[source]

SST2.analysis.data_plot.plot_scatter(df, x, y, hue=None, x_label=None, y_label=None, quant=None, s=10, color=None, linewidth=0, label=None, legend='auto', alpha=None, max_data=50000)[source]

Plot a scatter plot.

Parameters:

dfpandas.DataFrame: Dataframe with the data to plot.
xstr: Name of the column with the x axis data.
ystr: Name of the column with the y axis data.
huestr, optional: Name of the column with the hue data. The default is None.
x_labelstr, optional: Label of the x axis. The default is None.
y_labelstr, optional: Label of the y axis. The default is None.
quantfloat, optional: Quantile to use to filter the data. The default is None.
sint, optional: Size of the points. The default is 10.
colorstr, optional: Color of the points. The default is None.
linewidthfloat, optional: Width of the points. The default is 0.
labelstr, optional: Label of the plot. The default is None.
legendstr, optional: Position of the legend. The default is “auto”.
alphafloat, optional: Transparency of the points. The default is None.
max_dataint, optional: Maximum number of data point to plot. The default is 50000.

Returns:

gmatplotlib.axes._subplots.AxesSubplot: Axes of the plot.

SST2.analysis.data_plot.plot_weight_RMSD(df, x='$Time\\;(\\mu s)$', hue='Temp (K)', ener='new_pot', time_ax_name='$Time\\;(\\mu s)$', final_weight_dict=None, max_data=50000, plot_weights=False)[source]

SST2.analysis.data_plot.read_SST2_data(generic_name, dt=0.004, full_sep=',', save_step_dcd=100000, lambda_T_ref=300.0)[source]

Read the sst2 data from the csv files. The data may be splited in several files if simulation had to restart. The function merge all the files in one dataframe.

Parameters:

generic_namestr: Generic name of the csv files (without the .csv extension).
dtfloat, optional: Time step in ps of the simulation. The default is 0.004 ps.
full_sepstr, optional: Separator used in the full csv file. The default is “,”.
save_step_dcdint, optional: Step number used in the dcd file. The default is 100000.
lambda_T_reffloat, optional: Reference temperature for the lambda. The default is None.

Returns:

df_allpandas.DataFrame: Dataframe with all the data.

SST2.analysis.data_plot.read_ST_data(generic_name, dt=0.004, fields=['Steps', 'Aim Temp (K)', 'E solute scaled (kJ/mole)', 'E solute not scaled (kJ/mole)', 'E solvent (kJ/mole)', 'E solvent-solute (kJ/mole)'], full_sep=',', save_step_dcd=100000, lambda_T_ref=None)[source]

Read the sst2 data from the csv files. The data may be splited in several files if simulation had to restart. The function merge all the files in one dataframe.

Parameters:

generic_namestr: Generic name of the csv files (without the .csv extension).
dtfloat, optional: Time step in ps of the simulation. The default is 0.004 ps.
fieldslist, optional: List of the fields to read in the csv files. The default is [ “Steps”, “Aim Temp (K)”, “E solute scaled (kJ/mole)”, “E solute not scaled (kJ/mole)”, “E solvent (kJ/mole)”, “E solvent-solute (kJ/mole)”, ].
full_sepstr, optional: Separator used in the full csv file. The default is “,”.
save_step_dcdint, optional: Step number used in the dcd file. The default is 100000.
lambda_T_reffloat, optional: Reference temperature for the lambda. The default is None.

SST2.analysis.data_plot.recompute_temp(df, ref_temp=300.0)[source]

SST2.analysis.trajectory module

SST2.analysis.trajectory.align_traj(md, ref, ref_Sel, tol_mass=0.1)[source]

SST2.analysis.trajectory.compute_native_contact(md, ref, sel='protein and not name H*', sel_2=None)[source]

SST2.analysis.trajectory.compute_pca(md, ref, sel='backbone', cum_var=0.8)[source]

SST2.analysis.trajectory.prepare_traj(md, ref_Sel, compound='fragments')[source]

SST2.analysis.trajectory.read_traj(start_pdb, generic_name)[source]

Module contents

SST2.analysis module.