Changed keyword names in plot_heatmap:

phase_diagram to phasediagram
energy_slope to energyslope
final_time to finaltime
tod_dif to dtof

docs/source/plotting_results.md

@@ -126,13 +126,13 @@ When running a set of KMC simulations at various operating conditions, 2D heatma
 
 - **`z`** (`str`): Magnitude to plot on the z-axis. Possible values:
   - `'tof'`: log₁₀ TOF (molec·s⁻¹·Å⁻²).
-  - `'tof_dif'`: Difference in log₁₀ TOF between two systems.
+  - `'dtof'`: Difference in log₁₀ TOF between two systems.
   - `'selectivity'`: Selectivity (%).
   - `'coverage'`: Coverage (%).
-  - `'phase_diagram'`: Most dominant surface species.
-  - `'final_time'`: log₁₀ Final time (s).
+  - `'phasediagram'`: Most dominant surface species.
+  - `'finaltime'`: log₁₀ Final time (s).
   - `'final_energy'`: Final energy (eV·Å⁻²).
-  - `'energy_slope'`: Energy slope (eV·Å⁻²·step⁻¹).
+  - `'energyslope'`: Energy slope (eV·Å⁻²·step⁻¹).
   - `'issues'`: To check for issues.
 
 Depending on the type of plot (`z`), some additional parameters might be needed (see below).
@@ -200,7 +200,7 @@ plt.show()
 
 #### TOF difference
 
-`z = 'tof_dif'`, plot the TOF difference (∆TOF, in molec·s⁻¹·Å⁻²) of a given gas-phase species between two simulations.
+`z = 'dtof'`, plot the TOF difference (∆TOF, in molec·s⁻¹·Å⁻²) of a given gas-phase species between two simulations.
 
 **Additional parameters:**
 
@@ -217,7 +217,7 @@ Optional:
   - `'time'` (based on simulated time)
   - `'nevents'` (based on the number of events)
 
-**Note:** The `min_molec` parameter is not allowed in `z = 'tof_dif'` plots.
+**Note:** The `min_molec` parameter is not allowed in `z = 'dtof'` plots.
 
 **Example:**
 
@@ -234,7 +234,7 @@ plot_heatmap(
     scan_path_ref='simulation_results_reference',
     x='pressure_CH4',
     y='pressure_CO2',
-    z='tof_dif',
+    z='dtof',
     gas_spec='H2',
     min_molec=0,
     analysis_range=[50, 100],
@@ -359,7 +359,7 @@ plt.show()
 
 #### Phase diagram
 
-`z = 'phase_diagram'`, plot the most dominant surface species.
+`z = 'phasediagram'`, plot the most dominant surface species.
 
 **Additional parameters:**
 
@@ -387,7 +387,7 @@ plot_heatmap(
     scan_path='simulation_results',
     x='pressure_CH4',
     y='pressure_CO2',
-    z='phase_diagram',
+    z='phasediagram',
     min_coverage=50.0,
     site_type='tC',
     surf_spec_values={
@@ -421,7 +421,7 @@ plt.show()
 
 #### Final time
 
-`z = 'final_time'`, plot the final time of the simulation (in s).
+`z = 'finaltime'`, plot the final time of the simulation (in s).
 
 **Additional parameters:**
 
@@ -443,7 +443,7 @@ plot_heatmap(
     scan_path='simulation_results',
     x='pressure_CH4',
     y='pressure_CO2',
-    z='final_time',
+    z='finaltime',
     levels=np.logspace(-5, 7, num=13),
     verbose=True,
     auto_title=True,
@@ -460,7 +460,7 @@ plt.show()
 
 #### Energy slope
 
-`z = 'energy_slope'`, plot the slope of the lattice energy (in eV·Å⁻²·step⁻¹).
+`z = 'energyslope'`, plot the slope of the lattice energy (in eV·Å⁻²·step⁻¹).
 
 A high slope of the lattice energy indicates that the simulation might have not reached the steady state.
 
@@ -485,7 +485,7 @@ plot_heatmap(
     scan_path='simulation_results',
     x='pressure_CH4',
     y='pressure_CO2',
-    z='energy_slope',
+    z='energyslope',
     range_type='nevents',
     auto_title=True,
     show_points=False,
@@ -585,7 +585,7 @@ for n, site_type in enumerate(['tC', 'tM', 'Pt']):
 
 for n, site_type in enumerate(['tC', 'tM', 'Pt']):
     plot_heatmap(
-        ax=axs[2, n], scan_path=scan_path, x=x_variable, y=y_variable, z="phase_diagram",
+        ax=axs[2, n], scan_path=scan_path, x=x_variable, y=y_variable, z="phasediagram",
         min_coverage=min_coverage, site_type=site_type,
         surf_spec_values={
             'CH3': 0.5, 'CH3_Pt': 0.5, 'CH2': 0.5, 'CH2_Pt': 0.5, 'CH': 0.5, 'CH_Pt': 0.5, 'C': 0.5,
@@ -611,7 +611,7 @@ plot_heatmap(
     auto_title=auto_title, show_points=show_points, show_colorbar=show_colorbar)
 
 plot_heatmap(
-    ax=axs[3, 1], scan_path=scan_path, x=x_variable, y=y_variable, z='final_time',
+    ax=axs[3, 1], scan_path=scan_path, x=x_variable, y=y_variable, z='finaltime',
     levels=np.logspace(-7, 7, num=15), auto_title=auto_title, show_points=show_points, show_colorbar=show_colorbar)
 
 # Hide axis labels of intermediate subplots

docs/source/reading_output_files.md

@@ -59,14 +59,14 @@ All KMC results can be obtained from the different `KMCOutput` attributes.
 ### Simulated time
 
 - **`time`** (*np.ndarray*): Simulated time (in seconds).
-- **`final_time`** (*float*): Final simulated time (in seconds).
+- **`finaltime`** (*float*): Final simulated time (in seconds).
 
 ### Lattice energy
 
 - **`energy`** (*np.ndarray*): Lattice energy (in eV·Å⁻²).
 - **`av_energy`** (*float*): Average lattice energy (in eV·Å⁻²).
 - **`final_energy`** (*float*): Final lattice energy (in eV·Å⁻²).
-- **`energy_slope`** (*float*): Slope of the lattice energy over the number of events (in eV·Å⁻²·step⁻¹). A high value may indicate that the simulation has not reached steady-state.
+- **`energyslope`** (*float*): Slope of the lattice energy over the number of events (in eV·Å⁻²·step⁻¹). A high value may indicate that the simulation has not reached steady-state.
 
 ### Molecules consumed/produced and TOF
 
@@ -158,5 +158,5 @@ for site_type in kmc_output.site_types:
 ### Energy slope
 
 ```python
-print(f"Energy slope: {kmc_output.energy_slope:.3e} eV·Å⁻²·step⁻¹")
+print(f"Energy slope: {kmc_output.energyslope:.3e} eV·Å⁻²·step⁻¹")
 ```

examples/DRM_on_PtHfC/dtof_heatmap.py

@@ -10,7 +10,7 @@
     scan_path_ref='simulation_results_reference',
     x='pressure_CH4',
     y='pressure_CO2',
-    z='tof_dif',
+    z='dtof',
     gas_spec='H2',
     min_molec=0,
     analysis_range=[50, 100],

examples/DRM_on_PtHfC/energyslope_heatmap.py

@@ -8,7 +8,7 @@
     scan_path='simulation_results',
     x='pressure_CH4',
     y='pressure_CO2',
-    z='energy_slope',
+    z='energyslope',
     range_type='nevents',
     auto_title=True,
     show_points=False,

examples/DRM_on_PtHfC/finaltime_heatmap.py

@@ -9,7 +9,7 @@
     scan_path='simulation_results',
     x='pressure_CH4',
     y='pressure_CO2',
-    z='final_time',
+    z='finaltime',
     levels=np.logspace(-5, 7, num=13),
     verbose=True,
     auto_title=True,

examples/DRM_on_PtHfC/multiple_heatmaps.py

@@ -37,7 +37,7 @@
 
 for n, site_type in enumerate(['tC', 'tM', 'Pt']):
     plot_heatmap(
-        ax=axs[2, n], scan_path=scan_path, x=x_variable, y=y_variable, z="phase_diagram",
+        ax=axs[2, n], scan_path=scan_path, x=x_variable, y=y_variable, z="phasediagram",
         min_coverage=min_coverage, site_type=site_type,
         surf_spec_values={
             'CH3': 0.5, 'CH3_Pt': 0.5, 'CH2': 0.5, 'CH2_Pt': 0.5, 'CH': 0.5, 'CH_Pt': 0.5, 'C': 0.5,
@@ -63,7 +63,7 @@
     auto_title=auto_title, show_points=show_points, show_colorbar=show_colorbar)
 
 plot_heatmap(
-    ax=axs[3, 1], scan_path=scan_path, x=x_variable, y=y_variable, z='final_time',
+    ax=axs[3, 1], scan_path=scan_path, x=x_variable, y=y_variable, z='finaltime',
     levels=np.logspace(-7, 7, num=15), auto_title=auto_title, show_points=show_points, show_colorbar=show_colorbar)
 
 # Hide axis labels of intermediate subplots

examples/DRM_on_PtHfC/phasediagram_heatmap.py

@@ -8,7 +8,7 @@
     scan_path='simulation_results',
     x='pressure_CH4',
     y='pressure_CO2',
-    z='phase_diagram',
+    z='phasediagram',
     min_coverage=50.0,
     site_type='tC',
     surf_spec_values={

zacrostools/detect_issues.py

@@ -4,7 +4,7 @@
 
 def detect_issues(path, analysis_range):
 
-    energy_slope_threshold = 5.0e-10  # eV/Ų/step
+    energyslope_threshold = 5.0e-10  # eV/Ų/step
     time_linear_fit_threshold = 0.95
 
     def reduce_size(time, energy, nevents, size=100):
@@ -25,7 +25,7 @@ def reduce_size(time, energy, nevents, size=100):
     # Check for a positive or negative trend in energy using linear regression
     coeffs_energy = np.polyfit(nevents_reduced, energy_reduced, 1)
     slope_energy = coeffs_energy[0]
-    energy_trend = abs(slope_energy) > energy_slope_threshold
+    energy_trend = abs(slope_energy) > energyslope_threshold
 
     # Perform linear regression on time vs. nevents
     coeffs_time = np.polyfit(nevents_reduced, time_reduced, 1)

zacrostools/kmc_output.py

@@ -31,7 +31,7 @@ class KMCOutput:
         Number of events occurred.
     time : np.ndarray
         Simulated time (in seconds).
-    final_time : float
+    finaltime : float
         Final simulated time (in seconds).
     energy : np.ndarray
         Lattice energy (in eV·Å⁻²).
@@ -125,9 +125,9 @@ def __init__(self, path: str, analysis_range: Union[list, None] = None, range_ty
 
         self.nevents = data_specnum[:, 1]
         self.time = data_specnum[:, 2]
-        self.final_time = data_specnum[-1, 2]
+        self.finaltime = data_specnum[-1, 2]
         self.energy = data_specnum[:, 4] / self.area  # in eV/Å2
-        self.energy_slope = abs(np.polyfit(self.nevents, self.energy, 1)[0])  # in eV/Å2/step
+        self.energyslope = abs(np.polyfit(self.nevents, self.energy, 1)[0])  # in eV/Å2/step
         self.final_energy = data_specnum[-1, 4] / self.area
         self.av_energy = self.get_average(array=self.energy, weights=weights)