ZacrosResults¶

API¶

class ZacrosResults(job)¶

A Class for handling Zacros Results.

ok()¶

Check if the execution of the associated job was successful or not. See Job.ok for more information.

get_zacros_version()¶

Return the zacros’s version from the ‘general_output.txt’ file.

get_reaction_network()¶

Return the reactions from the ‘general_output.txt’ file.

provided_quantities()¶

Return the provided quantities from the specnum_output.txt file in a form of a dictionary. Below is shown an example of the specnum_output.txt for a zacros calculation.

Entry   Nevents        Time   Temperature       Energy   O*  CO*     O2     CO   CO2
    1         0   0.000E+00   5.00000E+02   -4.089E+01   11   12      0      0     0
    2        88   1.000E-01   5.00000E+02   -7.269E+01   22   17    -21    -36    31
    3       176   2.000E-01   5.00000E+02   -9.139E+01   29   19    -41    -71    64
    4       247   3.000E-01   5.00000E+02   -1.041E+02   34   20    -57    -99    91

For this example, this function will return:

{
   "Entry":[1, 2, 3, 4],
   "Nevents":[0, 88, 176, 247],
   "Time":[0.000E+00, 1.000E-01, 2.000E-01, 3.000E-01],
   "Temperature":[5.00000E+02, 5.00000E+02, 5.00000E+02, 5.00000E+02],
   "Energy":[-4.089E+01, -7.269E+01, -9.139E+01, -1.041E+02],
   "O*":[11, 22, 29, 34],
   "CO*":[12, 17, 19, 20],
   "O2":[0, -21, -41, -57],
   "CO":[0, -36, -71, -99],
   "CO2":[0, 31, 64, 91]
}

number_of_lattice_sites()¶

Return the number of lattice sites from the ‘general_output.txt’ file.

gas_species_names()¶

Return the gas species names from the ‘general_output.txt’ file.

surface_species_names()¶

Return the surface species names from the ‘general_output.txt’ file.

site_type_names()¶

Return the site types from the ‘general_output.txt’ file.

number_of_snapshots()¶

Return the number of configurations from the ‘history_output.txt’ file.

number_of_process_statistics()¶

Return the number of process statistics from the ‘procstat_output.txt’ file.

elementary_steps_names()¶

Return the names of elementary steps from the ‘procstat_output.txt’ file.

lattice_states(last=None)¶

Return the configurations from the ‘history_output.txt’ file.

last_lattice_state()¶

Return the last configuration from the ‘history_output.txt’ file.

plot_lattice_states(data, pause=-1, show=True, ax=None, close=False, time_perframe=0.5, file_name=None)¶

Uses matplotlib to visualize the lattice states as an animation

• data – List of LatticeState objects to plot
• pause – After showing the figure, it will wait pause-seconds before refreshing.
• show – Enables showing the figure on the screen.
• ax – The axes of the plot. It contains most of the figure elements: Axis, Tick, Line2D, Text, Polygon, etc., and sets the coordinate system. See matplotlib.axes.
• close – Closes the figure window after pause time.
• time_perframe – Sets the time interval between frames in seconds.
• file_name – Saves the figures to the file file_name-<id> (the corresponding id on the list replaces the <id>). The format is inferred from the extension, and by default, .png is used.

plot_molecule_numbers(species_name, pause=-1, show=True, ax=None, close=False, file_name=None, normalize_per_site=False, derivative=False)¶

Uses matplotlib to visualize the Molecule Numbers an animation

• species_name – List of species names to show, e.g., ["CO*", "CO2"]
• pause – After showing the figure, it will wait pause-seconds before refreshing. This can be used for crude animation.
• show – Enables showing the figure on the screen.
• ax – The axes of the plot. It contains most of the figure elements: Axis, Tick, Line2D, Text, Polygon, etc., and sets the coordinate system. See matplotlib.axes.
• close – Closes the figure window after pause time.
• file_name – Saves the figure to the file file_name. The format is inferred from the extension, and by default, .png is used.
• normalize_per_site – Divides the molecule numbers by the total number of sites in the lattice.
• derivative – Plots the first derivative.

get_process_statistics()¶

Return the statistics from the ‘procstat_output.txt’ file in a form of a list of dictionaries. Below is shown an example of the procstat_output.txt for a zacros calculation.

      Overall        CO_ads  O2_react_ads        CO_oxi
configuration     1             0          0.00
        0.000         0.000         0.000         0.000
            0             0             0             0
configuration     2           250          0.01
        0.039         0.043         0.044         0.000
          250           108           118            24

For this example, this function will return:

[
  {
    'configuration_number': 1,
    'total_number_of_events': 0,
    'time': 0.0,
    'average_waiting_time': {
                              'CO_ads': 0.0,
                              'O2_react_ads': 0.0,
                              'CO_oxi': 0.0
                            },
    'number_of_events': {
                          'CO_ads': 0,
                          'O2_react_ads': 0,
                          'CO_oxi': 0
                        },
    'occurence_frequency': {
                             'CO_ads': 0.0,
                             'O2_react_ads': 0.0,
                             'CO_oxi': 0.0
                           }
  },
  {
    'configuration_number': 2,
    'total_number_of_events': 250,
    'time': 0.01,
    'average_waiting_time': {
                              'CO_ads': 0.043
                              'O2_react_ads': 0.044
                              'CO_oxi': 0.0
                            },
    'number_of_events': {
                          'CO_ads': 108,
                          'O2_react_ads': 118,
                          'CO_oxi': 24
                        },
    'occurence_frequency': {
                             'CO_ads': 10800.0,
                             'O2_react_ads': 11800.0,
                             'CO_oxi': 2400.0
                           }
  }
]

The occurence_frequency is calculated as number_of_events/time.

plot_process_statistics(data, key, log_scale=False, pause=-1, show=True, ax=None, close=False, file_name=None)¶

Uses matplotlib to visualize the process statistics an animation

• data – List of process statistics to plot. See function get_process_statistics().
• key – Key to plot, e.g., 'average_waiting_time', 'average_waiting_time'. See function get_process_statistics().
• log_scale – Use log scale for the x axis.
• pause – After showing the figure, it will wait pause-seconds before refreshing.
• show – Enables showing the figure on the screen.
• ax – The axes of the plot. It contains most of the figure elements: Axis, Tick, Line2D, Text, Polygon, etc., and sets the coordinate system. See matplotlib.axes.
• close – Closes the figure window after pause time.
• file_name – Saves the figures to the file file_name-<id> (the corresponding id on the list replaces the <id>). The format is inferred from the extension, and by default, .png is used.

get_TOFs(nbatch=20, confidence=0.99)¶

Returns the TOF (mol/sec/site) calculated by the batch-means stopping method. See Hashemi et al., J.Chem. Phys. 144, 074104 (2016)

• nbatch – Number of batches to use.
• confidence – Confidence level to use in the criterion to determine if the steady-state was reached.

The simulation output is divided into an ensemble of contiguous batches where the TOF is computed. The average value and the standard deviation of the TOF ensemble are evaluated as follows:

\[<TOF> = \frac{1}{n}\sum_{i=1}^n TOF_i \qquad \sigma_{TOF}\sqrt{\frac{1}{n-1}\sum_{i=1}^n\left(TOF_i-<TOF>\right)}\]

where \(n = \text{nbatch}\). Confidence intervals are estimated to assess the steady-state behavior of the simulation:

\[<TOF>\pm t_{n-1,1-\frac{\delta}{2}}\frac{\sigma_{TOF}}{\sqrt{n}}\]

The function returns True when the steady-state is reached. This happens if the following equation is satisfied:

\[t_{n-1,1-\frac{\delta}{2}}\frac{\sigma_{TOF}}{\sqrt{n}} \lt \epsilon\]

Here the convergence criteria is \(\epsilon=1-\text{confidence}\)