Pyteomics documentation v4.5b3

pylab_aux - auxiliary functions for plotting with pylab

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pylab_aux - auxiliary functions for plotting with pylab

This module serves as a collection of useful routines for data plotting with matplotlib.

Generic plotting

plot_line() - plot a line.

scatter_trend() - plot a scatter plot with a regression line.

plot_function_3d() - plot a 3D graph of a function of two variables.

plot_function_contour() - plot a contour graph of a function of two variables.

Spectrum visualization

plot_spectrum() - plot a single spectrum (m/z vs intensity).

annotate_spectrum() - plot and annotate peaks in MS/MS spectrum.

mirror() - create a mirror plot of two spectra (using spectrum_utils).

FDR control

plot_qvalue_curve() - plot the dependence of q-value on the amount of PSMs (similar to a ROC curve).

Dependencies

This module requires matplotlib. Optional dependencies: adjustText, spectrum_utils.


class pyteomics.pylab_aux.SpectrumUtilsColorScheme(colors)[source]

Bases: object

Context manager that temporarily changes spectrum_utils.plot.colors.

__init__(colors)[source]

Initialize self. See help(type(self)) for accurate signature.

pyteomics.pylab_aux.annotate_spectrum(spectrum, peptide, *args, **kwargs)[source]

Plot a spectrum and annotate matching fragment peaks.

Parameters:
  • spectrum (dict) – A spectrum as returned by Pyteomics parsers. Needs to have ‘m/z array’ and ‘intensity array’ keys.
  • peptide (str) – A modX sequence.
  • backend (str, keyword only, optional) – One of {‘default’, ‘spectrum_utils’, ‘spectrum_utils.iplot’}. The spectrum_utils backend requires installing spectrum_utils. The spectrum_utils.iplot backend requires installing spectrum_utils[iplot].
  • ion_types (Container, keyword only, optional) – Ion types to be considered for annotation. Default is (‘b’, ‘y’).
  • precursor_charge (int, keyword only, optional) – If not specified, an attempt is made to extract it from spectrum.
  • maxcharge (int, keyword only, optional) – Maximum charge state for fragment ions to be considered. Default is precursor_charge - 1.
  • colors (dict, keyword only, optional) – Keys are ion types, values are colors to plot the annotated peaks with. Default depends on backend.
  • ftol (float, keyword only, optional) – A fixed m/z tolerance value for peak matching. Alternative to rtol.
  • rtol (float, keyword only, optional) – A relative m/z error for peak matching. Default is 10 ppm.
  • aa_mass (dict, keyword only, optional) – A dictionary of amino acid residue masses.
  • text_kw (dict, keyword only, optional) – Keyword arguments for pylab.text().
  • xlabel (str, keyword only, optional) – Label for the X axis. Default is “m/z”.
  • ylabel (str, keyword only, optional) – Label for the Y axis. Default is “intensity”.
  • title (str, keyword only, optional) – The title. Empty by default.
  • ax (matplotlib.pyplot.Axes, keyword only, optional) – Axes to draw the spectrum.
  • *args – Passed to the plotting backend.
  • **kwargs – Passed to the plotting backend.
  • centroided (bool, keyword only, optional) – Passed to plot_spectrum(). Only works with default backend.
  • ion_comp (dict, keyword only, optional) – A dictionary defining ion compositions to override pyteomics.mass.std_ion_comp. Only works with default backend.
  • mass_data (dict, keyword only, optional) – A dictionary of element masses to override pyteomics.mass.nist_mass. Only works with default backend.
  • adjust_text (bool, keyword only, optional) – Adjust the overlapping text annotations using adjustText. Only works with default backend.
  • adjust_kw (dict, keyword only, optional) – Keyword arguments for adjust_text(). Only works with default backend.
  • remove_precursor_peak (bool, keyword only, optional) – Remove precursor peak from spectrum before annotation. Default is False. Only works with spectrum_utils backend.
  • min_intensity (float, keyword only, optional) – Remove low-intensity peaks; this is a factor of maximum peak intensity. Default is 0 (no filtering). Only works with spectrum_utils and spectrum_utils.iplot backends.
  • max_num_peaks (int or None, keyword only, optional) – Remove low-intensity peaks; this is the number of peaks to keep. Default is None (no filtering). Only works with spectrum_utils and spectrum_utils.iplot backends.
  • scaling (one of {‘root’, ‘log’, ‘rank’} or None, keyword only, optional) – Scaling to apply to peak intensities. Only works with spectrum_utils and spectrum_utils.iplot backends.
  • max_intensity (float or None, keyword only, optional) – Intensity of the most intense peak relative to which the peaks will be scaled (the default is None, which means that no scaling relative to the most intense peak will be performed). Only works with spectrum_utils and spectrum_utils.iplot backends.
  • peak_assignment (one of {‘most_intense’, ‘nearest_mz’}, keyword only, optional) – In case multiple peaks occur within the given mass window around a theoretical peak, only a single peak will be annotated with the fragment type. Default is ‘most_intense’. Only works with spectrum_utils and spectrum_utils.iplot backends.
  • modifications (dict, optional) –

    A dict of variable modifications as described in spectrum_utils documentation.

    Note

    You don’t need to provide this if your peptide is a modX sequence and you supply aa_mass.

    Note

    To apply static modifications, provide aa_mass with modified masses.

Returns:

out

Return type:

matplotlib.pyplot.Axes

pyteomics.pylab_aux.mirror(spec_top, spec_bottom, peptide=None, spectrum_kws=None, ax=None, **kwargs)[source]

Create a mirror plot of two (possible annotated) spectra using spectrum_utils.

Note

Requires spectrum_utils.

Parameters:
  • spec_top (dict) – A spectrum as returned by Pyteomics parsers. Needs to have ‘m/z array’ and ‘intensity array’ keys.
  • spec_bottom (dict) – A spectrum as returned by Pyteomics parsers. Needs to have ‘m/z array’ and ‘intensity array’ keys.
  • peptide (str or None, optional) – A modX sequence. If provided, the peaks will be annotated as peptide fragments.
  • spectrum_kws (dict or None, optional) – Passed to spectrum_utils.plot.mirror().
  • ax (matplotlib.pyplot.Axes or None, optional) – Passed to spectrum_utils.plot.mirror().
  • xlabel (str, keyword only, optional) – Label for the X axis. Default is “m/z”.
  • ylabel (str, keyword only, optional) – Label for the Y axis. Default is “intensity”.
  • title (str, keyword only, optional) – The title. Empty by default.
  • **kwargs (same as for annotate_spectrum() for spectrum_utils backends.) –
Returns:

out

Return type:

matplotlib.pyplot.Axes

pyteomics.pylab_aux.plot_function_3d(x, y, function, **kwargs)[source]

Plot values of a function of two variables in 3D.

More on 3D plotting in pylab:

http://www.scipy.org/Cookbook/Matplotlib/mplot3D

Parameters:
  • x (array_like of float) – The plotting range on X axis.
  • y (array_like of float) – The plotting range on Y axis.
  • function (function) – The function to plot.
  • plot_type ({'surface', 'wireframe', 'scatter', 'contour', 'contourf'}, keyword only, optional) – The type of a plot, see scipy cookbook for examples. The default value is ‘surface’.
  • num_contours (int) – The number of contours to plot, 50 by default.
  • xlabel (str, keyword only, optional) – The X axis label. Empty by default.
  • ylabel (str, keyword only, optional) – The Y axis label. Empty by default.
  • zlabel (str, keyword only, optional) – The Z axis label. Empty by default.
  • title (str, keyword only, optional) – The title. Empty by default.
  • **kwargs – Passed to the respective plotting function.
pyteomics.pylab_aux.plot_function_contour(x, y, function, **kwargs)[source]

Make a contour plot of a function of two variables.

Parameters:
  • y (x,) – The positions of the nodes of a plotting grid.
  • function (function) – The function to plot.
  • filling (bool) – Fill contours if True (default).
  • num_contours (int) – The number of contours to plot, 50 by default.
  • ylabel (xlabel,) – The axes labels. Empty by default.
  • title (str, optional) – The title. Empty by default.
  • **kwargs – Passed to pylab.contour() or pylab.contourf().
pyteomics.pylab_aux.plot_line(a, b, xlim=None, *args, **kwargs)[source]

Plot a line y = a * x + b.

Parameters:
  • a (float) – The slope of the line.
  • b (float) – The intercept of the line.
  • xlim (tuple, optional) – Minimal and maximal values of x. If not given, pylab.xlim() will be called.
  • *args – Passed to pylab.plot() after x and y values.
  • **kwargs – Passed to pylab.plot().
Returns:

out – The line object.

Return type:

matplotlib.lines.Line2D

pyteomics.pylab_aux.plot_qvalue_curve(qvalues, *args, **kwargs)[source]

Plot a curve with q-values on the X axis and corresponding PSM number (starting with 1) on the Y axis.

Parameters:
  • qvalues (array-like) – An array of q-values for sorted PSMs.
  • xlabel (str, keyword only, optional) – Label for the X axis. Default is “q-value”.
  • ylabel (str, keyword only, optional) – Label for the Y axis. Default is “# of PSMs”.
  • title (str, keyword only, optional) – The title. Empty by default.
  • *args – Given to pylab.plot() after x and y.
  • **kwargs – Given to pylab.plot().
Returns:

out

Return type:

matplotlib.lines.Line2D

pyteomics.pylab_aux.plot_spectrum(spectrum, *args, **kwargs)[source]

Plot a spectrum, assuming it is a dictionary containing “m/z array” and “intensity array”.

Parameters:
  • spectrum (dict) – A dictionary, as returned by pyteomics MS data parsers. Must contain “m/z array” and “intensity array” keys with decoded arrays.
  • backend (str, keyword only, optional) – One of {‘default’, ‘spectrum_utils’, ‘spectrum_utils.iplot’}. The spectrum_utils backend requires installing spectrum_utils. The spectrum_utils.iplot backend requires installing spectrum_utils[iplot].
  • xlabel (str, keyword only, optional) – Label for the X axis. Default is “m/z”.
  • ylabel (str, keyword only, optional) – Label for the Y axis. Default is “intensity”.
  • title (str, keyword only, optional) – The title. Empty by default.
  • centroided (bool, keyword only, optional) – Works only for the default backend. If True (default), peaks of the spectrum are plotted using pylab.bar(). If False, the arrays are simply plotted using pylab.plot().
  • *args – When using default backend: given to pylab.plot() or pylab.bar() (depending on centroided).
  • **kwargs – When using default backend: given to pylab.plot() or pylab.bar() (depending on centroided).
  • min_intensity (float, keyword only, optional) – Remove low-intensity peaks; this is a factor of maximum peak intensity. Default is 0 (no filtering). Only works with spectrum_utils and spectrum_utils.iplot backends.
  • max_num_peaks (int or None, keyword only, optional) – Remove low-intensity peaks; this is the number of peaks to keep. Default is None (no filtering). Only works with spectrum_utils and spectrum_utils.iplot backends.
  • scaling (one of {‘root’, ‘log’, ‘rank’} or None, keyword only, optional) – Scaling to apply to peak intensities. Only works with spectrum_utils and spectrum_utils.iplot backends.
  • max_intensity (float or None, keyword only, optional) – Intensity of the most intense peak relative to which the peaks will be scaled (the default is None, which means that no scaling relative to the most intense peak will be performed). Only works with spectrum_utils and spectrum_utils.iplot backends.
Returns:

out

Return type:

matplotlib.pyplot.Axes

pyteomics.pylab_aux.scatter_trend(x, y=None, **kwargs)[source]

Make a scatter plot with a linear regression.

Parameters:
  • x (array_like of float) – 1-D array of floats. If y is omitted, x must be a 2-D array of shape (N, 2).
  • y (array_like of float, optional) – 1-D arrays of floats. If y is omitted or None, x must be a 2-D array of shape (N, 2).
  • plot_trend (bool, optional) – If True then plot a trendline (default).
  • plot_sigmas (bool, optional) – If True then plot confidence intervals of the linear fit. False by default.
  • show_legend (bool, optional) – If True, a legend will be shown with linear fit equation, correlation coefficient, and standard deviation from the fit. Default is True.
  • title (str, optional) – The title. Empty by default.
  • ylabel (xlabel,) – The axes labels. Empty by default.
  • alpha_legend (float, optional) – Legend box transparency. 1.0 by default
  • scatter_kwargs (dict, optional) – Keyword arguments for pylab.scatter(). Empty by default.
  • plot_kwargs (dict, optional) – Keyword arguments for plot_line(). By default, sets xlim and label.
  • legend_kwargs (dict, optional) – Keyword arguments for pylab.legend(). Default is {'loc': 'upper left'}.
  • sigma_kwargs (dict, optional) – Keyword arguments for pylab.plot() used for sigma lines. Default is {'color': 'red', 'linestyle': 'dashed'}.
  • sigma_values (iterable, optional) – Each value will be multiplied with standard error of the fit, and the line shifted by the resulting value will be plotted. Default is range(-3, 4).
  • regression (callable, optional) – Function to perform linear regression. Will be given x and y as arguments. Must return a 4-tuple: (a, b, r, stderr). Default is pyteomics.auxiliary.linear_regression().
Returns:

out – A (scatter_plot, trend_line, sigma_lines, legend) tuple.

Return type:

tuple

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