isotopylog.EDistribution.plot

EDistribution.plot(ax=None, nT=300, param=1, eps=1e-06, ed={'fmt': 'o'}, ld={}, fbd={'alpha': 0.5})

Generates an Arrhenius plot of a given parameter.

Parameters:
  • ax (None or plt.axis) – Axis for plotting results; defaults to None.
  • nT (int) – The number of temperature points to plot for Arrhenius fit predictions.
  • param (int) –

    The parameter of interest for making Arrhenius plot, specific to each model as follows:

    'Hea14': [ln(kc), ln(kd), ln(k2)]

    'HH21': [ln(k_mu), ln(k_sig)]

    'PH12': [ln(k), intercept]

    'SE15': [ln(k1), ln(k_dif_single), ln([pair]_0/[pair]_eq)]

    For eample, to make an Arrhenius plot of ln(kc) for ‘Hea14’ models, pass param = 1.

  • eps (float) – The amount to perturb each parameter when numerically calculating the derivative of ln(k) with respect to each parameter. Used for calculating a Jacobian to propagate parameter uncertainty. Defaults to 1e-6.
  • ed (dictionary) – Dictionary of keyward arguments to pass for plotting the experimental data. Must contain keywords compatible with matplotlib.pyplot.errorbar. Defaults to dictionary with ‘fmt’ = ‘o’.
  • ld (dictionary) – Dictionary of keyward arguments to pass for plotting the mean of the Arrhenius model fit line. Must contain keywords compatible with matplotlib.pyplot.plot. Defaults to empty dictionary.
  • fbd (dictionary) –

    Dictionary of keyward arguments to pass for plotting the Arrhenius model uncertaint range. Must contain keywords compatible with matplotlib.pyplot.errorbar. Defaults to dictionary with ‘alpha’

    = 0.5..
Returns:

ax – Updated axis containing results.
Return type:

plt.axis
Raises:

ValueError – If passed param is outside of the range of existing parameters (e.g., >2 for ‘HH21’ models).

See also

isotopylog.kDistribution.plot()
Class method for plotting rate distributions for ‘HH21’ model types.

Examples

Basic implementation, assuming ipl.EDistribution instance ed exists and contains data of model type ‘HH21’:

#import modules
import isotopylog as ipl
import matplotlib.pyplot as plt

#make figure
fig, ax = plt.subplots(2, 1, sharex = True)

#plot results
ed.plot(ax = ax[0], param = 1) #to plot mu_E
ed.plot(ax = ax[1], param = 2) #to plot sig_E
../../_images/ed_1.png

Similar implementation, but now putting in stylistic keyword args:

#import modules
import isotopylog as ipl
import matplotlib.pyplot as plt

#make figure
fig, ax = plt.subplots(2, 1, sharex = True)

#define plotting style
ld = {'linewidth':2, 'c':'k'}

#plot results
ed.plot(ax = ax[0], param = 1, ld = ld) #to plot mu_E
ed.plot(ax = ax[1], param = 2, ld = ld) #to plot sig_E
../../_images/ed_2.png