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Creating energy profile diagrams for publication
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Mohamed Abdelaleem
Creating energy profile diagrams for publication
PyEnergyDiagrams
I use my own script. I have posted it on GitHub now.
This is the final result:
It is quite easy to use, on the GitHub readme there is a small tutorial. You have to create an instance of ED class and then add the energy levels to it.
from energydiagram import ED
diagram = ED()
diagram.add_level(0,'Separated Reactants')
diagram.add_level(-5.4,'mlC1')
diagram.add_level(-15.6,'mlC2','last',)
diagram.add_level(28.5,'mTS1',color='g')
diagram.add_level(-9.7,'mCARB1')
diagram.add_level(-19.8,'mCARB2','last')
diagram.add_level(20,'mCARBX','last')
To add the links between the levels you can have a look to the IDs of the level using this the argument of the function plot show_IDs=True:
You can export it just saving the plot from the GUI button or programmatically in different formats for publications using matplotlib backend.
I prefer to export the plot in .pgf and then I import it in the LaTeX document this allow a full control over fonts and appearance of the figure.
For reaching better quality you have to change a little bit the autogenerated .pgf file.
The first thing you have to do is to find and replace − with -.
Later you can follow the procedure in this answer so that the font of the figure matches with the font size and type of the document.
I use my own script. I have posted it on GitHub now.This is the final result:
It is quite easy to use, on the GitHub readme there is a small tutorial. You have to create an instance of ED class and then add the energy levels to it.
from energydiagram import EDdiagram = ED()diagram.add_level(0,'Separated Reactants')diagram.add_level(-5.4,'mlC1')diagram.add_level(-15.6,'mlC2','last',)diagram.add_level(28.5,'mTS1',color='g')diagram.add_level(-9.7,'mCARB1')diagram.add_level(-19.8,'mCARB2','last')diagram.add_level(20,'mCARBX','last')
To add the links between the levels you can have a look to the IDs of the level using this the argument of the function plot show_IDs=True:
You can export it just saving the plot from the GUI button or programmatically in different formats for publications using matplotlib backend. I prefer to export the plot in .pgf and then I import it in the LaTeX document this allow a full control over fonts and appearance of the figure.
For reaching better quality you have to change a little bit the autogenerated .pgf file.
The first thing you have to do is to find and replace − with -.
Later you can follow the procedure in this answer so that the font of the figure matches with the font size and type of the document.
I have no experience with pgf, and from Wikipedia I get that it is a bitmap file format. While I dont use LaTeX much anymore, I dont see how a bitmap would be superior to a vector image. But maybe this isnt the right place to discuss this. But I will certainly look into your script.More
PyEnergyDiagrams
I use my own script. I have posted it on GitHub now. This is the final result:
It is quite easy to use, on the GitHub readme there is a small tutorial. You have to create an instance of ED class and then add the energy levels to it.
To add the links between the levels you can have a look to the IDs of the level using this the argument of the function plot
show_IDs=True:And then you can use:
Export to pgf,pdf and svg
You can export it just saving the plot from the GUI button or programmatically in different formats for publications using matplotlib backend. I prefer to export the plot in
.pgfand then I import it in the LaTeX document this allow a full control over fonts and appearance of the figure.For reaching better quality you have to change a little bit the autogenerated .pgf file.
The first thing you have to do is to find and replace − with -.
Later you can follow the procedure in this answer so that the font of the figure matches with the font size and type of the document.
PyEnergyDiagrams
I use my own script. I have posted it on GitHub now.This is the final result:
It is quite easy to use, on the GitHub readme there is a small tutorial. You have to create an instance of ED class and then add the energy levels to it.
To add the links between the levels you can have a look to the IDs of the level using this the argument of the function plot
show_IDs=True:And then you can use:
Export to pgf,pdf and svg
You can export it just saving the plot from the GUI button or programmatically in different formats for publications using matplotlib backend. I prefer to export the plot in
.pgfand then I import it in the LaTeX document this allow a full control over fonts and appearance of the figure.For reaching better quality you have to change a little bit the autogenerated .pgf file.
The first thing you have to do is to find and replace − with -.
Later you can follow the procedure in this answer so that the font of the figure matches with the font size and type of the document.
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