# Plotting heatmaps
When running a set of KMC simulations at various operating conditions, 2D heatmaps can be created using various specialized `plot_X` functions such as `plot_tof` or `plot_selectivity`.
### Parameters
#### Mandatory parameters for all plot types
- **`ax`** (`matplotlib.axes.Axes`): Axis object where the contour plot should be created.
- **`scan_path`** (`str`): Path of the directory containing all the scan jobs.
- **`x`** (`str`): Magnitude to plot on the x-axis. Possible values:
- `'pressure_X'`, where `X` is a gas species.
- `'temperature'`.
- `'total_pressure'`.
- **`y`** (`str`): Magnitude to plot on the y-axis. Possible values:
- `'pressure_Y'`, where `Y` is a gas species.
- `'temperature'`.
- `'total_pressure'`.
Depending on the type of plot, some additional parameters might be needed (see below).
#### Optional parameters
- **`cmap`** (`str`): Colormap used to map scalar data to colors. Accepts colormap instances or registered names.
**Default**: depends on the function.
- **`show_points`** (`bool`): Displays grid points as black dots if `True`.
**Default**: `False`.
- **`show_colorbar`** (`bool`): Displays the colorbar if `True`.
**Default**: `True`.
- **`auto_title`** (`bool`): Automatically generates titles for subplots if `True`.
**Default**: `False`.
### Heatmap types
#### TOF (Turnover Frequency)
Plot the TOF (in molec·s⁻¹·Å⁻²) of a given gas-phase species.
**Additional parameters:**
Mandatory:
- **`gas_spec`** (`str`): Name of the gas-phase species for the TOF difference calculation.
Optional:
- **`min_molec`** (`int`): Minimum total production in both main and reference simulations to consider the result valid.
**Default**: `1`.
- **`weights`** (`str`): Weights for the averages. Possible values:
- `'time'`
- `'events'`
- `None` (all weights are set to 1)
**Default**: `None`.
- **`levels`** (`list` or `np.ndarray`): Contour levels to use in the plot. When provided, TOF values are clipped to the minimum and maximum values in this list.
- **`analysis_range`** (`list`): Portion of the simulation data to analyze.
**Default**: `[0, 100]`.
- **`range_type`** (`str`): The type of window to apply. Possible values:
- `'time'` (based on simulated time)
- `'nevents'` (based on the number of events)
**Default**: `'time'`.
- **`show_max`** (`bool`): Display a golden `'*'` marker at the point with the highest TOF.
**Default**: `False`.
**Example:**
```python
import numpy as np
import matplotlib.pyplot as plt
from zacrostools.plot_functions.tof import plot_tof
fig, axs = plt.subplots(1, figsize=(4.3, 3.5))
plot_tof(
ax=axs,
scan_path='simulation_results',
x='pressure_CH4',
y='pressure_CO2',
gas_spec='H2',
# Changed default is now 1, but we can override as needed:
min_molec=0,
# analysis_range is used to specify which portion of the simulation to analyze:
analysis_range=[50, 100],
range_type='time',
# If levels are not provided, the function auto-scales the TOF data:
levels=np.logspace(-1, 4, num=11),
auto_title=True,
show_points=False,
show_colorbar=True
)
plt.tight_layout()
plt.savefig('tof_heatmap.png', dpi=300, bbox_inches='tight', transparent=False)
plt.show()
```
#### ∆TOF (Delta TOF)
Plot the difference in Turnover Frequencies (TOF) between a main simulation and a reference simulation for a specified gas-phase species. The difference can be either *absolute* (`main - reference`) or *relative*.
**Additional parameters:**
Mandatory:
- **`gas_spec`** (`str`): Name of the gas-phase species for the TOF difference calculation.
- **`scan_path_ref`** (`str`): Path to the reference simulation directories.
Optional:
- **`difference_type`** (`str`): Type of TOF difference to compute. Must be either `'absolute'` or `'relative'`.
**Default**: `'absolute'`.
- **`scale`** (`str`): Determines the color scaling of the difference. Possible values:
- `'log'`
- `'lin'`
**Default**: `'log'`.
- **`min_molec`** (`int`): Minimum total production in both main and reference simulations to consider the result valid.
**Default**: `1`.
- **`max_dtof`** (`float`): Maximum absolute value for \(\Delta\)TOF. For relative differences, the default is 100. If provided, values above (or below the negative) limit are clipped.
- **`min_dtof`** (`float`): Threshold that defines the smallest nonzero \(\Delta\)TOF to display. By default, it’s set automatically (depending on `max_dtof`).
- **`nlevels`** (`int`): Number of discrete boundaries if you want to segment the \(\Delta\)TOF scale.
- `0`: use continuous normalization.
- Must be a positive odd integer (`>=3`) for discrete normalization.
**Default**: `0`.
- **`weights`** (`str`): Weights for the averages. Possible values:
- `'time'`
- `'events'`
- `None` (all weights are set to 1)
**Default**: `None`.
- **`analysis_range`** (`list`): Portion of the simulation data to analyze.
**Default**: `[0, 100]`.
- **`range_type`** (`str`): The type of window to apply. Possible values:
- `'time'`
- `'nevents'`
**Default**: `'time'`.
**Example:**
```python
import matplotlib.pyplot as plt
from zacrostools.plot_functions.dtof import plot_dtof
fig, ax = plt.subplots(1, figsize=(4.3, 3.5))
plot_dtof(
ax=ax,
scan_path='simulation_results',
scan_path_ref='reference_results',
x='pressure_CH4',
y='pressure_CO2',
gas_spec='H2',
difference_type='absolute',
scale='log',
min_molec=0,
nlevels=11,
analysis_range=[50, 100],
range_type='time',
auto_title=True,
show_points=False,
show_colorbar=True
)
plt.tight_layout()
plt.savefig('dtof_heatmap.png', dpi=300, bbox_inches='tight', transparent=False)
plt.show()
```
#### Selectivity (%)
Plot the selectivity (in %) of a **main product** with respect to one or more **side products**.
**Additional parameters:**
Mandatory:
- **`main_product`** (`str`): Name of the main product for the selectivity calculation.
- **`side_products`** (`list`): List of side products for the selectivity calculation.
Optional:
- **`min_molec`** (`int`): Minimum combined production (main + side products) required for a valid selectivity value.
**Default**: `1`.
- **`weights`** (`str`): Weights for the averages. Possible values:
- `'time'`
- `'events'`
- `None` (all weights are set to 1)
**Default**: `None`.
- **`levels`** (`list` or `np.ndarray`): Contour levels to use in the plot (e.g., `np.linspace(0, 100, 11, dtype=int)`). If provided, selectivity values are clipped to the minimum and maximum values in this list.
**Default**: `np.linspace(0, 100, 11, dtype=int)`.
- **`analysis_range`** (`list`): Portion of the simulation data to analyze.
**Default**: `[0, 100]`.
- **`range_type`** (`str`): The type of window to apply. Possible values:
- `'time'`
- `'nevents'`
**Default**: `'time'`.
- **`cmap`** (`str`): Colormap used to map scalar data to colors.
**Default**: `'Greens'`.
**Example:**
```python
import numpy as np
import matplotlib.pyplot as plt
from zacrostools.plot_functions.selectivity import plot_selectivity
fig, ax = plt.subplots(1, figsize=(4.3, 3.5))
plot_selectivity(
ax=ax,
scan_path='simulation_results',
x='pressure_CH4',
y='pressure_CO2',
main_product='H2',
side_products=['H2O'],
min_molec=100,
analysis_range=[50, 100],
range_type='time',
auto_title=True,
show_points=False,
show_colorbar=True
)
plt.tight_layout()
plt.savefig('selectivity_heatmap.png', dpi=300, bbox_inches='tight', transparent=False)
plt.show()
```
#### Coverage (%)
Plot the coverage (in %) of specified surface species (or total coverage) on a given site type.
**Additional parameters:**
Mandatory:
- **`surf_spec`** (`str` or `list`): Surface species to include in the coverage calculation.
- `'all'`: Total coverage (of all adsorbates) is shown.
- A single species name (`'CO*'`) or a list of species (`['CH*', 'C*']`) can also be provided.
Optional:
- **`site_type`** (`str`): The site type on which coverage is computed.
**Default**: `'default'`.
- **`weights`** (`str`): Weights for the averages. Possible values:
- `'time'`
- `'events'`
- `None` (all weights are set to 1)
**Default**: `None`.
- **`levels`** (`list` or `np.ndarray`): Contour levels to use in the plot (e.g., `np.linspace(0, 100, 11, dtype=int)`). If provided, coverage values are clipped to the minimum and maximum values in this list.
**Default**: `np.linspace(0, 100, 11, dtype=int)`.
- **`analysis_range`** (`list`): Portion of the simulation data to analyze.
**Default**: `[0, 100]`.
- **`range_type`** (`str`): The type of window to apply. Possible values:
- `'time'`
- `'nevents'`
**Default**: `'time'`.
- **`cmap`** (`str`): Colormap used to map scalar data to colors.
**Default**: `'Oranges'`.
**Example:**
```python
import numpy as np
import matplotlib.pyplot as plt
from zacrostools.plot_functions.coverage import plot_coverage
fig, ax = plt.subplots(1, figsize=(4.3, 3.5))
plot_coverage(
ax=ax,
scan_path='simulation_results',
x='pressure_CH4',
y='pressure_CO2',
surf_spec='all',
site_type='tC',
weights='time',
analysis_range=[50, 100],
range_type='time',
auto_title=True,
show_points=False,
show_colorbar=True
)
plt.tight_layout()
plt.savefig('coverage_heatmap.png', dpi=300, bbox_inches='tight', transparent=False)
plt.show()
```
#### Phase Diagram
Plot a phase diagram where each point is assigned a **dominant surface species** (or `NaN` if coverage is too low). The color encodes these dominant species, grouped according to user-provided or auto-generated labels.
**Additional parameters:**
*(No additional mandatory parameters are required.)*
Optional:
- **`site_type`** (`str`): Site type to consider when retrieving the dominant surface species.
**Default**: `'default'`.
- **`min_coverage`** (`float` or `int`): Minimum coverage threshold (%) for a species to be considered dominant.
**Default**: `50.0`.
- **`tick_labels`** (`dict`): Custom mapping for species grouping and colorbar labels. The keys are used as colorbar labels (e.g., `'$CH_{x}$'`), and the values are lists of species names (e.g., `['CH3', 'CH2', 'C']`). When not provided, the function automatically parses a `simulation_input.dat` file from the first simulation directory to assign each species to its own group.
- **`weights`** (`str`): Weights for the averages. Possible values:
- `'time'`
- `'events'`
- `None` (all weights are set to 1)
**Default**: `None`.
- **`analysis_range`** (`list`): Portion of the simulation data to analyze.
**Default**: `[0, 100]`.
- **`range_type`** (`str`): The type of window to apply. Possible values:
- `'time'`
- `'nevents'`
**Default**: `'time'`.
**Example:**
```python
import matplotlib.pyplot as plt
from zacrostools.plot_functions.phasediagram import plot_phasediagram
tick_labels = {
'$CH_{x}$': ['CH3', 'CH3_Pt', 'CH2', 'CH2_Pt', 'CH',
'CH_Pt', 'C', 'C_Pt'],
'$CHO/COH$': ['CHO', 'CHO_Pt', 'COH', 'COH_Pt'],
'$CO$': ['CO', 'CO_Pt'],
'$COOH$': ['COOH', 'COOH_Pt'],
'$CO_{2}$': ['CO2', 'CO2_Pt'],
'$H$': ['H', 'H_Pt'],
'$H_{2}O$': ['H2O', 'H2O_Pt'],
'$OH$': ['OH', 'OH_Pt'],
'$O$': ['O', 'O_Pt']
}
fig, ax = plt.subplots(1, figsize=(4.3, 3.5))
plot_phasediagram(
ax=ax,
scan_path='simulation_results',
x='pressure_CO2',
y='pressure_CH4',
site_type='tC',
min_coverage=50.0,
tick_labels=tick_labels,
weights='time',
analysis_range=[50, 100],
range_type='time',
auto_title=True,
show_points=False,
show_colorbar=True
)
plt.tight_layout()
plt.savefig('phase_diagram.png', dpi=300, bbox_inches='tight', transparent=False)
plt.show()
```
#### Final Time (s)
Plot the **final simulation time** (in seconds) for each job in a 2D heatmap. The color scale is logarithmically normalized.
**Additional parameters:**
*(No additional mandatory parameters are required.)*
Optional:
- **`levels`** (`list` or `np.ndarray`): Contour levels for the plot. If provided, final time values are clipped to the minimum and maximum in this list.
**Default**: `None`.
**Example:**
```python
import numpy as np
import matplotlib.pyplot as plt
from zacrostools.plot_functions.finaltime import plot_finaltime
fig, ax = plt.subplots(1, figsize=(4.3, 3.5))
plot_finaltime(
ax=ax,
scan_path='simulation_results',
x='pressure_CH4',
y='pressure_CO2',
levels=np.logspace(-5, 7, num=13),
show_points=False,
show_colorbar=True,
auto_title=True
)
plt.tight_layout()
plt.savefig('finaltime_heatmap.png', dpi=300, bbox_inches='tight', transparent=False)
plt.show()
```
#### Energy Slope
Plot the **energy slope** computed during the KMC simulation in a 2D heatmap. By default, data are shown on a logarithmic scale (using `LogNorm`).
**Additional parameters:**
*(No additional mandatory parameters are required.)*
Optional:
- **`levels`** (`list` or `np.ndarray`): Levels for normalization. If provided, the color scale is clipped between `min(levels)` and `max(levels)`.
**Default**: `np.logspace(-11, -8, num=7)`.
- **`analysis_range`** (`list`): Portion of the simulation data to analyze.
**Default**: `[0, 100]`.
- **`range_type`** (`str`): The dimension considered in the analysis. Possible values:
- `'time'`
- `'nevents'`
**Default**: `'time'`.
**Example:**
```python
import matplotlib.pyplot as plt
import numpy as np
from zacrostools.plot_functions.energyslope import plot_energyslope
fig, ax = plt.subplots(1, figsize=(4.3, 3.5))
plot_energyslope(
ax=ax,
scan_path='simulation_results',
x='pressure_CH4',
y='pressure_CO2',
analysis_range=[50, 100],
range_type='time',
auto_title=True,
show_points=False,
show_colorbar=True
)
plt.tight_layout()
plt.savefig('energyslope_heatmap.png', dpi=300, bbox_inches='tight', transparent=False)
plt.show()
```
#### Issues
Check each simulation for **issues** and visualize the results in a yes/no heatmap.
**Additional parameters:**
Mandatory:
- **`analysis_range`** (`list`): Portion of the simulation data to analyze (e.g., `[0, 100]`). If `None`, it is internally set to `[0, 100]`.
Optional:
- **`range_type`** (`str`): The dimension used for slicing the simulation data. Possible values:
- `'time'`
- `'nevents'`
**Default**: `'time'`.
- **`cmap`** (`str`): Colormap used for plotting.
**Default**: `'RdYlGn'`.
- **`verbose`** (`bool`): If `True`, prints the paths of simulations that contain issues.
**Default**: `False`.
**Example:**
```python
import matplotlib.pyplot as plt
from zacrostools.plot_functions.issues import plot_issues
fig, ax = plt.subplots(1, figsize=(4.3, 3.5))
plot_issues(
ax=ax,
scan_path='simulation_results',
x='pressure_CH4',
y='pressure_CO2',
analysis_range=[50, 100],
range_type='time',
verbose=True,
auto_title=True,
show_colorbar=True,
show_points=False
)
plt.tight_layout()
plt.savefig('issues_heatmap.png', dpi=300, bbox_inches='tight', transparent=False)
plt.show()
```
### Figure with multiple heatmaps
Different specialized plotting functions can be combined in a single figure, as illustrated below. Each function (`plot_tof`, `plot_coverage`, `plot_phasediagram`, `plot_selectivity`, `plot_finaltime`, etc.) handles a particular type of heatmap.
```python
import numpy as np
import matplotlib.pyplot as plt
# Import the specialized plotting functions
from zacrostools.plot_functions.tof import plot_tof
from zacrostools.plot_functions.coverage import plot_coverage
from zacrostools.plot_functions.phasediagram import plot_phasediagram
from zacrostools.plot_functions.selectivity import plot_selectivity
from zacrostools.plot_functions.finaltime import plot_finaltime
# General parameters
scan_path = 'simulation_results'
x_variable = 'pressure_CH4'
y_variable = 'pressure_CO2'
analysis_range = [50, 100] # (in %) Ignore first X% of the total simulated time (e.g., initial equilibration)
range_type = 'time'
weights = 'time'
min_molec_tof = 0
min_molec_selectivity = 100
min_coverage = 50 # (in %) Minimum coverage threshold for phase diagrams
auto_title = True
show_points = False
show_colorbar = True
fig, axs = plt.subplots(4, 3, figsize=(8.8, 8), sharey='row', sharex='col')
# --- First row: TOF for different products ---
for n, product in enumerate(['CO', 'H2', 'H2O']):
plot_tof(
ax=axs[0, n],
scan_path=scan_path,
x=x_variable,
y=y_variable,
gas_spec=product,
min_molec=min_molec_tof,
analysis_range=analysis_range,
range_type=range_type,
levels=np.logspace(-1, 4, num=11),
auto_title=auto_title,
show_points=show_points,
show_colorbar=show_colorbar
)
# --- Second row: Coverage for different site types ---
for n, site_type in enumerate(['tC', 'tM', 'Pt']):
plot_coverage(
ax=axs[1, n],
scan_path=scan_path,
x=x_variable,
y=y_variable,
surf_spec='all',
site_type=site_type,
analysis_range=analysis_range,
range_type=range_type,
weights=weights,
auto_title=auto_title,
show_points=show_points,
show_colorbar=show_colorbar
)
# --- Third row: Phase diagram for different site types ---
# Example grouping of species into labeled categories for the colorbar
tick_labels = {
'$CH_{x}$': ['CH3', 'CH3_Pt', 'CH2', 'CH2_Pt', 'CH', 'CH_Pt', 'C', 'C_Pt'],
'$CHO/COH$': ['CHO', 'CHO_Pt', 'COH', 'COH_Pt'],
'$CO$': ['CO', 'CO_Pt'],
'$COOH$': ['COOH', 'COOH_Pt'],
'$CO_{2}$': ['CO2', 'CO2_Pt'],
'$H$': ['H', 'H_Pt'],
'$H_{2}O$': ['H2O', 'H2O_Pt'],
'$OH$': ['OH', 'OH_Pt'],
'$O$': ['O', 'O_Pt']
}
for n, site_type in enumerate(['tC', 'tM', 'Pt']):
plot_phasediagram(
ax=axs[2, n],
scan_path=scan_path,
x=x_variable,
y=y_variable,
site_type=site_type,
min_coverage=min_coverage,
tick_labels=tick_labels,
analysis_range=analysis_range,
range_type=range_type,
auto_title=auto_title,
show_points=show_points,
show_colorbar=show_colorbar
)
# --- Fourth row: Selectivity and Final Time ---
# Selectivity
plot_selectivity(
ax=axs[3, 0],
scan_path=scan_path,
x=x_variable,
y=y_variable,
main_product='H2',
side_products=['H2O'],
min_molec=min_molec_selectivity,
analysis_range=analysis_range,
range_type=range_type,
auto_title=auto_title,
show_points=show_points,
show_colorbar=show_colorbar
)
# Final time
plot_finaltime(
ax=axs[3, 1],
scan_path=scan_path,
x=x_variable,
y=y_variable,
levels=np.logspace(-7, 7, num=15),
auto_title=auto_title,
show_points=show_points,
show_colorbar=show_colorbar
)
# Hide the third subplot in the last row
axs[3, 2].axis('off')
# Optional: clean up labels in intermediate subplots
for i in range(3):
for j in range(3):
axs[i, j].set_xlabel('')
for i in range(3):
for j in range(1, 3):
axs[i, j].set_ylabel('')
plt.tight_layout()
plt.savefig('multiple_heatmaps.png', dpi=300, bbox_inches='tight', transparent=False)
plt.show()
```
---
### Customization
The size of axis ticks and font size of axis labels and titles can be adjusted with Matplotlib. Below is an example (shown here for a single heatmap, but the same approach applies to multi-plot figures as well):
```python
import numpy as np
import matplotlib.pyplot as plt
from zacrostools.plot_functions.tof import plot_tof
fig, axs = plt.subplots(1, figsize=(4.3, 3.5))
cp = plot_tof(
ax=axs,
scan_path='simulation_results',
x='pressure_CH4',
y='pressure_CO2',
gas_spec='H2',
min_molec=0,
analysis_range=[50, 100],
range_type='time',
levels=np.logspace(-1, 4, num=11),
auto_title=True,
show_points=False,
show_colorbar=False
)
# Adjust size of axis ticks
axs.tick_params(axis='both', which='major', labelsize=14)
# Adjust font size of axis labels
axs.set_xlabel(axs.get_xlabel(), fontsize=18)
axs.set_ylabel(axs.get_ylabel(), fontsize=18)
# Adjust font size and position of the title
axs.set_title(axs.get_title(), fontsize=20, loc='center', pad=-170)
# Create colorbar and adjust the size of its tick labels
cbar = plt.colorbar(cp, ax=axs)
cbar.ax.tick_params(labelsize=14)
plt.tight_layout()
plt.savefig('tof_heatmap_custom.png', dpi=300, bbox_inches='tight', transparent=False)
plt.show()
```
```
