Datasets¶

SAXSDiffDataset¶

class moldynplot.dataset.SAXSDataset.SAXSDiffDataset(dataset_cache=None, **kwargs)¶

Bases: moldynplot.dataset.SAXSDataset.SAXSDataset

Represents Small Angle X-ray Scattering difference data.

SAXSExperimentDataset¶

class moldynplot.dataset.SAXSDataset.SAXSExperimentDataset(scale=False, **kwargs)¶

Bases: moldynplot.dataset.SAXSDataset.SAXSDataset

Represents Small Angle X-ray Scattering experimental data.

Parameters:	infile (str) – Path to input file, may contain environment variables verbose (int) – Level of verbose output kwargs (dict) – Additional keyword arguments

SAXSTimeSeriesDataset¶

See moldynplot.dataset.TimeSeriesDataset.SAXSTimeSeriesDataset.

Command-line interface¶

SequenceDataset¶

class moldynplot.dataset.SequenceDataset.SequenceDataset(calc_pdist=False, outfile=None, interactive=False, **kwargs)¶

Bases: moldynplot.myplotspec.Dataset.Dataset

Represents data that is a function of amino acid sequence.

sequence_df¶

DataFrame – DataFrame whose index corresponds to amino acid residue number in the form XAA:# and whose columns are a series of quantities specific to each residue. Standard errors of these quantities may be represented by adjacent columns with ‘ se’ appended to their names.

               r1     r1 se        r2     r2 se  ...
residue
GLN:2    2.451434  0.003734  5.041334  0.024776  ...
TYR:3    2.443613  0.004040  5.138383  0.025376  ...
LYS:4    2.511626  0.004341  5.589428  0.026236  ...
...      ...       ...       ...       ...       ...

Parameters:

infile{s} (list) – Path(s) to input file(s); may contain environment variables and wildcards use_indexes (list) – Residue indexes to select from DataFrame, once DataFrame has already been loaded calc_pdist (bool) – Calculate probability distribution using calc_pdist() dataset_cache (dict) – Cache of previously-loaded Datasets interactive (bool) – Provide iPython prompt and reading and processing data verbose (int) – Level of verbose output kwargs (dict) – Additional keyword arguments

static construct_argparser(parser_or_subparsers=None, **kwargs)¶

Adds arguments to an existing argument parser, constructs a subparser, or constructs a new parser

Parameters:	(ArgumentParser, _SubParsersAction, (parser_or_subparsers) – optional): If ArgumentParser, existing parser to which arguments will be added; if _SubParsersAction, collection of subparsers to which a new argument parser will be added; if None, a new argument parser will be generated kwargs (dict) – Additional keyword arguments
Returns:	ArgumentParser – Argument parser or subparser

classmethod get_cache_key(**kwargs)¶

Generates key for dataset cache.

See SequenceDataset for argument details.

Returns:	tuple – Cache key; contains arguments sufficient to reconstruct dataset

read(**kwargs)¶

Reads sequence from one or more infiles into a DataFrame.

Extends Dataset with option to read in residue indexes.

calc_pdist(**kwargs)¶

Calculates probability distribution across sequence.

Parameters:

df (DataFrame) – DataFrame; probability distribution will be calculated for each column using rows as data points pdist_kw (dict) – Keyword arguments used to configure probability distribution calculation pdist_kw[columns] (list) – Columns for which to calculate probability distribution pdist_kw[mode] (ndarray, dict) – Method of calculating probability distribution; eventually will support ‘hist’ for histogram and ‘kde’ for kernel density estimate, though presently only kde is implremented pdist_kw[grid] (ndarray, dict, optional) – Grid on which to calculate kernel density estimate; may be a single ndarray that will be applied to all columns or a dictionary whose keys are column names and values are ndarrays corresponding to the grid for each column; for any column for which grid is not specified, a grid of 1000 points between the minimum value minus three times the standard deviation and the maximum value plots three times the standard deviation will be used pdist_kw[bandwidth] (float, dict, str, optional) – Bandwidth to use for kernel density estimates; may be a single float that will be applied to all columns or a dictionary whose keys are column names and values are floats corresponding to the bandwidth for each column; for any column for which bandwidth is not specified, the standard deviation will be used; alternatively may be ‘se’, in which case the standard error of each value will be used verbose (int) – Level of verbose output kwargs (dict) – Additional keyword arguments

Returns:

dict – Dictionary whose keys are columns in df and values are DataFrames whose indexes are the grid for that column and contain a single column ‘probability’ containing the normalized probability at each grid point

ChemicalShiftDataset¶

class moldynplot.dataset.SequenceDataset.ChemicalShiftDataset(delays=None, calc_relax=False, calc_pdist=False, outfile=None, interactive=False, **kwargs)¶

Bases: moldynplot.dataset.SequenceDataset.SequenceDataset

Represents an NMR peak list data.

Parameters:

infile{s} (list) – Path(s) to input file(s); may contain environment variables and wildcards delays (list) – Delays corresponding to series of infiles; used to name columns of merged sequence DataFrame use_indexes (list) – Residue indexes to select from DataFrame, once DataFrame has already been loaded calc_pdist (bool) – Calculate probability distribution pdist_kw (dict) – Keyword arguments used to configure probability distribution calculation dataset_cache (dict) – Cache of previously-loaded Datasets verbose (int) – Level of verbose output kwargs (dict) – Additional keyword arguments

static construct_argparser(parser_or_subparsers=None, **kwargs)¶

Adds arguments to an existing argument parser, constructs a subparser, or constructs a new parser

Parameters:	(ArgumentParser, _SubParsersAction, (parser_or_subparsers) – optional): If ArgumentParser, existing parser to which arguments will be added; if _SubParsersAction, collection of subparsers to which a new argument parser will be added; if None, a new argument parser will be generated kwargs (dict) – Additional keyword arguments
Returns:	ArgumentParser – Argument parser or subparser

read(**kwargs)¶

Reads sequence from one or more infiles into a DataFrame.

Extends Dataset with option to read in residue indexes.

calc_relax(**kwargs)¶

Calculates relaxation rates.

Parameters:

df (DataFrame) – DataFrame; probability distribution will be calculated for each column using rows as data points relax_kw (dict) – Keyword arguments used to configure relaxation rate calculation relax_kw[kind] (str) – Kind of relaxation rate being calculated; will be used to name column relax_kw[intensity_method] (str) – Metric to use for peak instensity; may be ‘height’ (default) or ‘volume’ relax_kw[error_method] (str) – Metric to use for error calculation; may be ‘rmse’ for root-mean-square error (default) or ‘mae’ for mean absolute error relax_kw[n_synth_datasets] (int) – Number of synthetic datasets to use for error calculation

Returns:

DataFrame – Sequence DataFrame with additional columns for relaxation rate and standard error

RelaxDataset¶

class moldynplot.dataset.SequenceDataset.RelaxDataset(calc_pdist=False, outfile=None, interactive=False, **kwargs)¶

Bases: moldynplot.dataset.SequenceDataset.SequenceDataset

Represents NMR relaxation data as a function of residue number.

Parameters:

infile{s} (list) – Path(s) to input file(s); may contain environment variables and wildcards use_indexes (list) – Residue indexes to select from DataFrame, once DataFrame has already been loaded calc_pdist (bool) – Calculate probability distribution pdist_kw (dict) – Keyword arguments used to configure probability distribution calculation dataset_cache (dict) – Cache of previously-loaded Datasets verbose (int) – Level of verbose output kwargs (dict) – Additional keyword arguments

static construct_argparser(parser_or_subparsers=None, **kwargs)¶

Adds arguments to an existing argument parser, constructs a subparser, or constructs a new parser

Parameters:	(ArgumentParser, _SubParsersAction, (parser_or_subparsers) – optional): If ArgumentParser, existing parser to which arguments will be added; if _SubParsersAction, collection of subparsers to which a new argument parser will be added; if None, a new argument parser will be generated kwargs (dict) – Additional keyword arguments
Returns:	ArgumentParser – Argument parser or subparser

write_for_relax(outfile, **kwargs)¶

Writes sequence DataFrame in format readable by relax.

Command-line interface¶

TimeSeriesDataset¶

class moldynplot.dataset.TimeSeriesDataset.TimeSeriesDataset(dt=None, toffset=None, downsample=None, calc_pdist=False, outfile=None, interactive=False, **kwargs)¶

Bases: moldynplot.myplotspec.Dataset.Dataset

Represents data as a function of time.

timeseries_df¶

DataFrame – DataFrame whose index corresponds to time as represented by frame number or chemical time and whose columns are a series of quantities as a function of time.

Parameters:

infile{s} (list) – Path(s) to input file(s); may contain environment variables and wildcards dt (float) – Time interval between points; units unspecified toffset (float) – Time offset to be added to all points (i.e. time of first point) downsample (int) – Interval by which to downsample points downsample_mode (str) – Method of downsampling; may be ‘mean’ or ‘mode’ calc_pdist (bool) – Calculate probability distribution pdist_key (str) – Column of which to calculate probability distribution kde_kw (dict) – Keyword arguments passed to sklearn.neighbors.KernelDensity; key argument is ‘bandwidth’ grid (ndarray) – Grid on which to calculate probability distribution interactive (bool) – Provide iPython prompt and reading and processing data verbose (int) – Level of verbose output kwargs (dict) – Additional keyword arguments todo (.) – Calculate pdist using histogram Verbose pdist

static construct_argparser(parser_or_subparsers=None, **kwargs)¶

Adds arguments to an existing argument parser, constructs a subparser, or constructs a new parser

Parameters:	(ArgumentParser, _SubParsersAction, (parser_or_subparsers) – optional): If ArgumentParser, existing parser to which arguments will be added; if _SubParsersAction, collection of subparsers to which a new argument parser will be added; if None, a new argument parser will be generated kwargs (dict) – Additional keyword arguments
Returns:	ArgumentParser – Argument parser or subparser

downsample(downsample, downsample_mode=u'mean', **kwargs)¶

Downsamples time series.

Parameters:	downsample (int) – Interval by which to downsample points downsample_mode (str) – Method of downsampling; may be ‘mean’ or ‘mode’ verbose (int) – Level of verbose output kwargs (dict) – Additional keyword arguments

calc_pdist(**kwargs)¶

Calcualtes probability distribution of time series.

Parameters:	pdist_kw (dict) – Keyword arguments used to configure probability distribution calculation verbose (int) – Level of verbose output kwargs (dict) – Additional keyword arguments

timeseries_to_sequence(**kwargs)¶

Calculates the mean and standard error over a timeseries.

Parameters:

{timeseries}d{ata}f{rame} (DataFrame) – Timeseries over which to calculate mean and standard error; if omitted looks for timeseries_df block_kw (dict) – Keyword arguments passed to fpblockaverager.FPBlockAverager block_kw[all_factors] (bool) – Use all factors by which the is divisible rather than only factors of two (dataset) – block_kw[min_n_blocks] (int) – Minimum number of blocks after transformation block_kw[max_cut] (float) – Maximum proportion of dataset of omit in transformation block_kw[fit_exp] (bool) – Fit exponential curve block_kw[fit_sig] (bool) – Fit sigmoid curve verbose (int) – Level of verbose output kwargs (dict) – Additional keyword arguments

Returns:

DataFrame – Sequence dataframe including mean and standard error for each column in timeseries_df

IREDTimeSeriesDataset¶

class moldynplot.dataset.TimeSeriesDataset.IREDTimeSeriesDataset(dt=None, toffset=None, downsample=None, calc_pdist=False, calc_mean=False, outfile=None, interactive=False, **kwargs)¶

Bases: moldynplot.dataset.TimeSeriesDataset.TimeSeriesDataset, moldynplot.dataset.SequenceDataset.IREDDataset

Represents iRED NMR relaxation data as a function of time and residue number.

Parameters:

infile{s} (list) – Path(s) to input file(s); may contain environment variables and wildcards dt (float) – Time interval between points; units unspecified toffset (float) – Time offset to be added to all points (i.e. time of first point) downsample (int) – Interval by which to downsample points downsample_mode (str) – Method of downsampling; may be ‘mean’ or ‘mode’ calc_pdist (bool) – Calculate probability distribution interactive (bool) – Provide iPython prompt and reading and processing data verbose (int) – Level of verbose output kwargs (dict) – Additional keyword arguments

static construct_argparser(parser_or_subparsers=None, **kwargs)¶

Adds arguments to an existing argument parser, constructs a subparser, or constructs a new parser

Parameters:	(ArgumentParser, _SubParsersAction, (parser_or_subparsers) – optional): If ArgumentParser, existing parser to which arguments will be added; if _SubParsersAction, collection of subparsers to which a new argument parser will be added; if None, a new argument parser will be generated kwargs (dict) – Additional keyword arguments
Returns:	ArgumentParser – Argument parser or subparser

static concatenate_timeseries(timeseries_dfs=None, relax_dfs=None, order_dfs=None, **kwargs)¶

Concatenates a series of iRED datasets.

Parameters:	relax_dfs (list) – DataFrames containing data from relax infiles order_dfs (list) – DataFrames containing data from order infiles verbose (int) – Level of verbose output kwargs (dict) – Additional keyword arguments
Returns:	df (DataFrame) – Averaged DataFrame including relax and order

read(**kwargs)¶

Reads iRED time series data from one or more infiles into a DataFrame.

NatConTimeSeriesDataset¶

class moldynplot.dataset.TimeSeriesDataset.NatConTimeSeriesDataset(downsample=None, calc_pdist=True, **kwargs)¶

Bases: moldynplot.dataset.TimeSeriesDataset.TimeSeriesDataset

Manages native contact datasets.

Parameters:

infile (str) – Path to input file, may contain environment variables usecols (list) – Columns to select from DataFrame, once dataframe has already been loaded dt (float) – Time interval between points; units unspecified toffset (float) – Time offset to be added to all points (i.e. time of first point) cutoff (float) – Minimum distance within which a contact is considered to be formed downsample (int) – Interval by which to downsample points using mode pdist (bool) – Calculate probability distribution verbose (int) – Level of verbose output kwargs (dict) – Additional keyword arguments

PRETimeSeriesDataset¶

class moldynplot.dataset.TimeSeriesDataset.PRETimeSeriesDataset(dt=None, toffset=None, downsample=None, calc_pdist=False, outfile=None, interactive=False, **kwargs)¶

Bases: moldynplot.dataset.TimeSeriesDataset.TimeSeriesDataset, moldynplot.dataset.SequenceDataset.RelaxDataset

Represents paramagnetic relaxation enhancement data as a function of time and residue number.

Parameters:

infile{s} (list) – Path(s) to input file(s); may contain environment variables and wildcards dt (float) – Time interval between points; units unspecified toffset (float) – Time offset to be added to all points (i.e. time of first point) downsample (int) – Interval by which to downsample points downsample_mode (str) – Method of downsampling; may be ‘mean’ or ‘mode’ calc_pdist (bool) – Calculate probability distribution pdist_key (str) – Column of which to calculate probability distribution kde_kw (dict) – Keyword arguments passed to sklearn.neighbors.KernelDensity; key argument is ‘bandwidth’ grid (ndarray) – Grid on which to calculate probability distribution interactive (bool) – Provide iPython prompt and reading and processing data verbose (int) – Level of verbose output kwargs (dict) – Additional keyword arguments

static construct_argparser(parser_or_subparsers=None, **kwargs)¶

Adds arguments to an existing argument parser, constructs a subparser, or constructs a new parser

Parameters:	(ArgumentParser, _SubParsersAction, (parser_or_subparsers) – optional): If ArgumentParser, existing parser to which arguments will be added; if _SubParsersAction, collection of subparsers to which a new argument parser will be added; if None, a new argument parser will be generated kwargs (dict) – Additional keyword arguments
Returns:	ArgumentParser – Argument parser or subparser

SAXSTimeSeriesDataset¶

class moldynplot.dataset.TimeSeriesDataset.SAXSTimeSeriesDataset(infile, address=u'saxs', downsample=None, calc_mean=False, calc_error=True, error_method=u'std', scale=False, **kwargs)¶

Bases: moldynplot.dataset.TimeSeriesDataset.TimeSeriesDataset, moldynplot.dataset.SAXSDataset.SAXSDataset

Manages Small Angle X-ray Scattering time series datasets.

Parameters:

infile (str) – Path to input file, may contain environment variables usecols (list) – Columns to select from DataFrame, once dataframe has already been loaded dt (float) – Time interval between points; units unspecified toffset (float) – Time offset to be added to all points (i.e. time of first point) downsample (int) – Interval by which to downsample points verbose (int) – Level of verbose output kwargs (dict) – Additional keyword arguments