Package SloppyCell :: Package ReactionNetworks :: Module Plotting
[hide private]

Module Plotting

source code

Functions [hide private]
 
PlotEigenvectors(eigVects, net=None, title=None) source code
 
plotStateSpaceTrajectoriesForVariables(traj, id1, id2, thresholds=None) source code
 
plotTrajectoriesForVariables(traj, ids=None, showLegend=True) source code
 
PlotTrajectoriesForExperiments(model, experiments, params=None, with_data=True, plotPts=100, overlap=.1, skip=1, showLegend=True) source code
 
PlotDataForExperiments(model, experiments, skip=1) source code
 
plot_model_data(model, expts=None, style='errorbars', show_legend=True, loc='upper left')
Plot the data in the given experiments for the given model.
source code
 
plot_model_results(model, expts=None, style='errorbars', show_legend=True, loc='upper left', plot_data=True, plot_trajectories=True, data_to_plot=None)
Plot the fits to the given experiments for the last cost evalution of the model.
source code
 
plot_ensemble_results(model, ensemble, expts=None, style='errorbars', show_legend=True, loc='upper left', plot_data=True, plot_trajectories=True)
Plot the fits to the given experiments over an ensemble.
source code
 
plot_trajectory(traj, vars=None, show_legend=True, loc='upper left', logx=False, logy=False) source code
 
plot_deriv_trajectory(traj, vars=None, show_legend=True, loc='upper left', logx=False, logy=False) source code
 
plot_ensemble_trajs(best_traj=None, mean_traj=None, std_traj=None, std_devs=1.0, vars=None, show_legend=True, loc='upper left')
Plot the results of a Network ensemble.
source code
Variables [hide private]
  logger = logging.getLogger('ReactionNetworks.Plotting')

Imports: sets, dist, entropy, sci, contourf, frange, fft2, rec2csv, ylabel, norm, MultipleLocator, mlab, rec_append_field, mpl, rc, thetagrids, cool, window_hanning, FormatStrFormatter, longest_contiguous_ones, get_plot_commands, fftn, ishold, Circle, spectral, YearLocator, axes, polyfit, axvline, irfftn, twiny, twinx, num2epoch, matplotlib, plot_singvals, l2norm, rgrids, legend, box, irfft2, levypdf, IndexDateFormatter, MO, LogLocator, broken_barh, HourLocator, l1norm, ginput, ColorWheel, FR, fftshift, num2date, silent_list, plot_eigval_spectrum, ifftn, plot, fromfunction_kw, PolarAxes, xticks, clabel, rk4, fftfreq, ifft2, vlines, flag, hold, sample, ma, Text, det, SU, DateLocator, SA, scatter, Normalize, spy, MinuteLocator, quiver, basic_symbols, figure, get_sparse_matrix, setp, DAILY, irefftn, autumn, rms_flat, get_scale_docs, gca, winter, gcf, gci, csd, RRuleLocator, hot, exception_to_str, eigvalsh, pinv, griddata, pink, reset_vals_cw, irefft2, colormaps, pylab_setup, TickHelper, barbs, xlim, MONTHLY, over, fft, window_none, Widget, disconnect, clf, stineman_interp, cla, strpdate2num, subplot_tool, SECONDLY, suptitle, get_backend, matrix_power, scipy, quiverkey, is_numlike, waitforbuttonpress, relativedelta, MonthLocator, tensorinv, dist_point_to_segment, bone, LogFormatter, poly_below, isvector, connect, PlotEigenvalueSpectrum, plot_eigvect, IndexLocator, mfuncC, rem, plotting, log2, date2num, rec_join, acorr, Line2D, semilogx, semilogy, yticks, epoch2num, copper, irfft, ranf, subplots_adjust, fill_between, Axes, MaxNLocator, rank, plot_priors, figimage, jet, figaspect, imshow, axhline, mean_flat, ispower2, LogFormatterExponent, ihfft, detrend_none, liaupunov, sum_flat, prctile_rank, segments_intersect, pcolor, xlabel, interactive, subplot, isinteractive, eigvals, close, clim, ylim, binary_repr, figlegend, demean, FixedFormatter, boxplot, SecondLocator, warnings, Button, amap, distances_along_curve, axvspan, FuncFormatter, WE, popd, text, random, colors, find, FigureCanvasBase, cholesky, title, axhspan, center_matrix, plotfile, get, gray, qr, bar, ioff, lstsq, loglog, rcParamsDefault, fftsurr, hfft, basic_colors, refftn, prctile, LogFormatterMathtext, imread, barh, DayLocator, Formatter, is_string_like, contour, NullFormatter, get_xyz_where, hist, bivariate_normal, detrend_linear, detrend, AutoLocator, refft2, WEEKLY, plot_eigvals, DateFormatter, Locator, LinAlgError, orth, xcorr, rec_drop_fields, prepca, load, cm, solve, figtext, norm_flat, table, cohere, normpdf, Rectangle, approx_real, rfftn, axis, switch_backend, longest_ones, path_length, basic_lines, csv2rec, colorbar, rfft2, eig, Polygon, conv, cond, WeekdayLocator, poly_between, getp, dedent, polyval, SubplotTool, drange, sys, ion, identity, refft, ifft, Figure, trapz, add_newdocs, rcParams, summer, hexbin, Arrow, savefig, detrend_mean, get_current_fig_manager, bench, hlines, save, Annotation, get_scale_names, psd, datestr2num, xscale, grid, vector_lengths, plot_date, pcolormesh, SloppyCell, spring, NullLocator, rcdefaults, rfft, rrule, matshow, MINUTELY, ifftshift, inside_poly, Artist, is_closed_polygon, iterable, LinearLocator, plt, arrow, stem, step, np, hsv, diagonal_matrix, mx2num, Residuals, ScalarFormatter, exp_safe, annotate, normalize, sqrtm, get_cmap, HOURLY, irefft, findobj, slopes, flatten, YEARLY, yscale, inv, specgram, pie, vals_cW, fill, Tester, TU, Slider, prism, TH, test, polar, draw, errorbar, delaxes, movavg, svd, colorbar_doc, base_repr, eigh, FixedLocator, tensorsolve, Network_mod, logging


Function Details [hide private]

plot_model_data(model, expts=None, style='errorbars', show_legend=True, loc='upper left')

source code 

Plot the data in the given experiments for the given model.

Note: You may need to run a Plotting.show() to display the plot.

Inputs:
  model: Model whose experiments to plot
  expts: List of experiment IDs to plot
  style: Style of plot. Currently supported options are:
      'errorbars': Plots points and bars for each data point
      'lines': Plots a continuous line for the data
  show_legend: Boolean that control whether or not to show the legend
  loc: Location of the legend. See help(Plotting.legend) for options.

plot_model_results(model, expts=None, style='errorbars', show_legend=True, loc='upper left', plot_data=True, plot_trajectories=True, data_to_plot=None)

source code 

Plot the fits to the given experiments for the last cost evalution of the
model.

Note: You may need to run a Plotting.show() to display the plot.

Inputs:
  model: Model whose results to plot
  expts: List of experiment IDs to plot, if None is specified, all 
         experiments are plotted
  style: Style of plot. Currently supported options are:
          'errorbars': Plots points and bars for each data point
          'lines': Plots a continuous line for the data
  show_legend: Boolean that control whether or not to show the legend
  loc: Location of the legend. See help(Plotting.legend) for options.
  plot_data: Boolean that controls whether the data is plotted
  plot_trajectories: Boolean that controls whether the trajectories are
                     plotted
  data_to_plot: If None, all data variables will be plotted. Otherwise,
                   pass a list of id's and only variables in  that list
                   will be plotted.

plot_ensemble_results(model, ensemble, expts=None, style='errorbars', show_legend=True, loc='upper left', plot_data=True, plot_trajectories=True)

source code 

Plot the fits to the given experiments over an ensemble. 

Note that this recalculates the cost for every member of the ensemble, so
 it may be very slow. Filtering correlated members from the ensemble is
 strongly recommended.

Inputs:
  model: Model whose results to plot
  ensemble: Parameter ensemble
  expts: List of experiment IDs to plot, if None is specified, all 
         experiments are plotted
  style: Style of plot. Currently supported options are:
          'errorbars': Plots points and bars for each data point
          'lines': Plots a continuous line for the data
  show_legend: Boolean that control whether or not to show the legend
  loc: Location of the legend. See help(Plotting.legend) for options.
  plot_data: Boolean that controls whether the data is plotted
  plot_trajectories: Boolean that controls whether the trajectories are
                     plotted

plot_ensemble_trajs(best_traj=None, mean_traj=None, std_traj=None, std_devs=1.0, vars=None, show_legend=True, loc='upper left')

source code 

Plot the results of a Network ensemble.

Inputs:
 best_traj -- Best-fit trajectory
 mean_traj -- Mean trajectory
 std_traj -- Trajectory of standard deviations
 std_devs -- Number of standard deviations to draw bounds at
 vars -- List of variable ids to plot the bounds for

 show_legend -- Boolean to show a legend or not
 loc -- Location code for legend