dlsmicro.backend.plot_tools

dlsmicro.backend.plot_tools.add_w_scaling(omega, scaling, w_b, placement)

Plot a given scaling on complex modulus plot to compared against the complex modulus of a sample.

Parameters:

• omega (1-d array) – Vector of frequency range covered by the complex modulus plotted in the plot
• scaling (list of float) – List of 2 floats, where the first number is numerator of fraction and second is denominator of fraction
• w_b (float) – Value of complex modulus plotted where scaling should appear on the plot
• placement (list of float) – First element in list is lower bound of scaling line, second element in list is upper bound of scaling line, where both elements are values between 0 and 1. The value of the first element should be less than the value of the second element

dlsmicro.backend.plot_tools.bootstrap_freq_sweep(df, quantity, replicate_identifier, n_bootstrap, estimator=<function mean>)

Gets bootstrap samples of an estimator for frequency (or time)

sweep data from a Dataframe. The Dataframe is assumed to contain a vector for a given quantity in which each vector replicate is labeled by a unique replicate identifier.

Parameters:

• df (DataFrame) – Dataframe containing table of results from DLS microrheology analysis for a single condition
• quantity (str) –

  Name of variable to plot as defined in the Dataframe replicate_identifier : str

  Name of quantity to average over

  n_bootstrap : int

  Number of points for bootstrap

• estimator (callable function) –

  Function for evaluating center of distribution

  Returns

• ------- –
• M_bootstrap (2-d array) – Matrix in which each row represents a frequency sweep

dlsmicro.backend.plot_tools.bootstrap_freq_sweep_ci(df, quantity, replicate_identifier, n_bootstrap, ci, estimator=<function mean>)

Gets bootstrap confidence interval for an estimator of

frequency sweep (or time sweep) data. Boot strap can be either a percentile bootstrap of an estimator of a studentized bootstrap of the mean

Parameters:

• df (DataFrame) – Dataframe containing table of results from DLS microrheology analysis for a single condition
• quantity (str) –

  Name of variable to plot as defined in the Dataframe replicate_identifier : str

  Name of quantity to average over

  n_bootstrap : int

  Number of points for bootstrap

  ci : float

  Percent of distribution included in error bars

• estimator (callable function, optional) –

  Function for evaluating center of distribution

  Returns

• ------- –
• ci_low (1-d array) – Vector of the lower bound of the confidence interval over entire frequency range.
• ci_high (1-d array) – Vector of the upper bound of the confidence interval over entire frequency range.

dlsmicro.backend.plot_tools.bootstrap_matrix_byrows(M, n_bootstrap, estimator)

Gets bootstrap samples of an estimator for frequency (or time)

sweep data from a matrix containing all vectors for a given quantity over all replicates.

Parameters:

• M (2-d array) –

  Matrix of all values for a given quantity over all replicates n_bootstrap : int

  Number of points for bootstrap

• estimator (callable function) –

  Function for evaluating center of distribution

  Returns

• ------- –
• M_bootstrap (2-d array) – Matrix in which each row represents a frequency sweep

dlsmicro.backend.plot_tools.df_to_matrix(df, quantity, replicate_identifier)

Construct a matrix from a Dataframe for a given vector-valued

quantity in which each vector replicate is labeled by a unique replicate identifier.

Parameters:

• df (DataFrame) – Dataframe containing table of results from DLS microrheology analysis for a single condition
• quantity (str) –

  Name of variable to plot as defined in the Dataframe replicate_identifier : str

  Name of quantity to average over

  Returns

• ------- –
• M (2-d array) – Matrix where each row is a replicate of the vector quantity.

dlsmicro.backend.plot_tools.plot_replicates_from_df(df, my_quantity, plot_ci=True, myci=68.0, estimator=<function mean>, color='m', ls='-', err_alpha=0.25, err_lw=2.5, identifier='replicate')

Plot a given quantity from the Dataframe, averaging across all replicates in that Dataframe.

Parameters:

• df (DataFrame) – Dataframe containing table of results from DLS microrheology analysis for a single condition
• my_quantity (str) – Name of variable to plot as defined in the Dataframe
• plot_ci (boolean, optional) – If True, plot error bars
• myci (float, optional) –

  Percent of distribution plotted in error bars estimator : callable function, optional

  Function for evaluating main plotted value

  color : str, optional

  Color of plotted line

  ls : str, optional

  Linestyle of plotted line

  err_alpha : float, optional

  Transparency level of error bars

  err_lw : float, optional

  Linewidth of error bar outlines

  identifier : str, optional

  Name of quantity to average over