Whenever I purposefully put H atoms inside a structure (with Fe and O in the structure as host atoms) as interstitials, I expect that it will be defined as an isolated atom with no bonds between it and the surrounding host atoms. However, depending on the location of the H interstitial, pyiron sometimes defines a bond between the additional H and the original O for lammps calculation.
This can be useful for the automatic detection of bonds. However, how can one control this feature whenever not needed?
I also believe there is a bug with the .define_bonds() function.
The inputs for the functions are:
species="O"
element_list=["H"]
cutoff_list=[2.0]
max_bond_list=1
bond_type_list=1
While I believe the max_bond_list and bond_type_list are supposed to be lists, it only works error-free if both are defined as integers instead because of the way they are defined. However, defining them as integers then messes up running the jobs because they should be iterables.
Related
I would like to use the package ggspectra but I can't figure out how to use it in means of data type(?). With the examples given with two_suns.spct it works, more or less, but when I want to use my own data which is w.length ~ Intensity/count, I can't get any plot with it. What do I have to do (with my own data)?
df[1:10, ]
Intensity w.length
1 0.00021348 1.235582e-21
2 0.00026164 1.008143e-21
3 0.00030980 8.514191e-22
4 0.00035796 7.368669e-22
5 0.00040612 6.494837e-22
6 0.00045428 5.806284e-22
7 0.00050244 5.249731e-22
8 0.00055060 4.790541e-22
9 0.00059876 4.405220e-22
10 0.00064693 4.077270e-22
(...)
I'm trying it via:
library(readxl)
library(ggplot2)
library(photobiology)
library(photobiologyWavebands)
library(ggspectra)
Lambda = h*c / E
h = 6.62607015e-34
c = 299792458
df$w.length = (h * c) / df$Energy_MeV
ggplot(df, aes(x = Energy_MeV, y = Intensity)) +
geom_line() +
The code line
ggplot(df) + geom_line()
does not work at all as I receive the information that aes() is necessary.
'ggspectra' is designed to work with spectral data stored in classes defined in package 'photobiology' as you noticed. These classes are based on data frame but store additional metadata in attributes and have strict expectations about the units used to express the spectral data, the units used to express wavelength and the names of the columns used to store them. This approach has pros and cons. Once we have created an object of one of these classes, when we pass it as argument to ggplot(), R dispatches a ggplot() method specific to these classes that "know" how to set aes() automatically. There are also autoplot() methods that build a whole ggplot object. A big advantage of keeping the metadata in attributes of the object were the data is stored is that this ensures their availability not only when plotting but for any other computations now and in the future, helping ensure reproducibility. This of course, requires additional work up front as we need to create an object belonging to a special class and store both data and metadata in it.
When designing these packages I did not expect they would be used for anything other than light and ultraviolet radiation, expressed either as energy in W m-2 or as photons in mol s-1 m-2, and wavelength in nm. Just for completness, I mention that when dealing with these units a data frame con be converted with the conversion constructor as.source_spct() into a source_spct object if the data are already expressed in the expected units and the column names follow the naming conventions. Alternatively, a source_spct object can be created with the source_spct() constructor by passing suitable vectors as arguments, similarly to how a data frame is created. Additional arguments can be passed to set the metadata attributes.
Neither of these constructors will work in this case as clearly the spectral data in the question is expressed is some other units or even is a different quantity.
I need some help in creating a VI that generates virtual or calculated channels based on several channels I measure.
e.g.
I measure voltage on several AI, lets say, ch A,B,C,D,E were B,C and E represent current on a shunt and would like to calculate a the power of the system
Q[A] = B+C
R[W] = A*Q
S[W] = D*E
T[W] = R+S
I would like to load the equations externally from a configuration file that may vary from one project to another equations would come in a format of a string Q=A+B , R= A*Q .....
*(during a run equation and channel count don't change - only when loading config).
The main issues that I am facing is that the inputs to each equation may have dependencies on virtual channels that do not have data yet
Was trying to use:
formula nodes/ Math scripts: https://zone.ni.com/reference/en-XX/help/371361R-01/lvconcepts/formula_nodes/
https://knowledge.ni.com/KnowledgeArticleDetails?id=kA03q000000x30HCAQ&l=en-IL
All data that should be chunked into a data stream (continues sampling) that can be presented on a Chart/Graph and saved to CSV/TDMS
do I need some additional packages?
I have tried the following based on the the example given - getting strange result
Answer
The elements you are looking for are not the Formula/Math Nodes but rather the:
Formula Parsing VIs
Using these VIs you are able to pass a calculation in the form of a string and an array of variable names and then evaluate the formula. This allows for run-time variable scripting, where most other nodes require compile time formula evaluation (With the exception of the python node).
Example
Example of using a very simple program to evaluate two different calculations using the same values and variables.
I'm trying to solve a non-deterministic problem with DEAP. the problem is that the module only evaluate new chromosome and uses the score kept in memory for old ones.
How can i set the module, so at each generation the ENTIRE population will be evaluated, and not just the new ones?
Thx
I don't think DEAP package can do that.
You can simply implement the algorithm on your own or find the new packages.
Anyway, check out my library contains most of the state-of-the-art meta-heuristic algorithms. It also evaluates the entire population in each generation.
https://github.com/thieunguyen5991/mealpy
You can modify 2-3 lines in your current chosen algorithm like below to force evaluation on all items. This can be done via copying from the source, to your local script, and then editing the invalid_individual flagged item check pre evaluation. Make sure in main you call local easimple and not algorithms.easimple to make the switch to local code.
If you are using easimple or eaMuPlusLambda, for example, you can find that function here in this file:
https://github.com/DEAP/deap/blob/master/deap/algorithms.py#L85
The 0th gen case here may not change(but can change anyway, unless your individuals come with a fitness already and you want to skip evaluation):
#(line 149 in above URL)
invalid_ind = [ind for ind in population if not ind.fitness.valid]
And then inside the generational process loop:
#(line 171 in url above):
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
Removing the invalid check will result in all items being passed to evaluation:
invalid_ind = [ind for ind in population] #149
...
invalid_ind = [ind for ind in offspring] #171
But keep the algorithms import! note you also need to change varAnd(case of easimple) to algorithms.varAnd to prevent a break.
offspring = algorithms.varAnd(offspring, toolbox, cxpb, mutpb)
I am new to modelica, and i don't have this much experience in it, but i got the basics of course. I am trying to model a micrfluidic network. The network consists of two sources of water and oil, controlled by two valves. The flow of the two mediums interact at a Tjunction and then into a tank or chamber. I don't care about the fluid properties of the mixture because its not my purpose. My question is how do redeclare two medium packages (water and oil) in one system component such as the Tjunction or a tank in order to simulate the system. In my real model, the two mediums doesn't meet, becuase every medium passes through the channels at a different time.
I attached the model with this message. Here's the link.
https://www.dropbox.com/s/yq6lg9la8z211uc/twomediumsv2.zip?dl=0
Thanks for the help .
I don't think you can redeclare a medium during simulation. In your case (where you don't need the mixing of the two fluids) you could create a new medium, for instance called OilWaterMixture, extending from Modelica.Media.Interfaces.PartialMedium.
If you look into the code of PartialMedium you'll see that it contains a lot of partial ("empty") functions that you should fill in in your new medium model. For example, in OilWaterMixture you should extend the function specificEnthalpy_pTX to return the specific enthalpy of your water/oil mixture, for a certain water/oil mixture (given by the mass fraction vector X). This could be done by adding the following model to the OilWaterMixture package:
redeclare function extends specificEnthalpy_pTX "Return specific enthalpy"
Oil = Modelica.Media.Incompressible.Examples.Essotherm650;
Water = Modelica.Media.Water.StandardWater;
algorithm
h_oil := Oil.h_pT(p,T);
h_water := Water.specificEnthalpy_pT(p,T);
h := X[0]*h_oil + X[1]*h_water;
end specificEnthalpy_pTX;
The mass fraction vector X is defined in PartialMedium and in OilWaterMixture you must define that it has two elements.
Again, since you are not going to actually use the mixing properties but only mass fraction vectors {0,1} or {1,0} the simple linear mixing equation should be adequate.
When you use OilWaterMixture in the various components, the error log will tell you which medium functions they need. So you probably don't need to extend all the partial functions in PartialMedium.
I want to store a list of variable names in a new local variable, such that I do not have to type a long list of variable names for each regression. I am using Stata 14.
E.g., I have the following 5 independent variables: a b c d e and one dependent variable: f
I don't want:
regress f a b c d e
But I want something like:
regress f allvar
How can I generate allvar?
Unfortunately, this does not work
local allvar a b c d e
The following works fine.
clear
set more off
sysuse auto
// first regressions
regress price mpg rep78 weight
// second regression
local allvars mpg rep78 weight
regress price `allvars'
Unless you show us something reproducible and/or more explicit, it's difficult to see what the problem is. A report only mentioning "does not work" is usually useless.
See also the keyword _all in help varlist.
You are using a local macro. If you are running the code by parts, then don't. You need to run the whole code, all at once. Read [P] macro, for details. An excerpt:
Local macros exist solely within the program or do-file in which they
are defined. If that program or do-file calls another program or
do-file, the local macros previously defined temporarily cease to
exist, and their existence is reestablished when the calling program
regains control. When a program or do-file ends, its local macros are
permanently deleted.
A common reason why your command sometimes "does not work" is that you ran your do-file line by line, rather than all in one go. A local macro is local to a session (hence the name). So if you ran the line local allvar a b c d e, then that will create that local macro and let it disapear as soon as Stata finished running that section of your .do file. There are two solutions:
You can get into the habit of running the definition of local macros and their use in one go. It is actually good practice to make many small .do files and make each .do file self-contained (see for example this excellent book), so you can easily just run the entire .do file each time you want to check or change something.
Alternatively, you can use global macros. These continue to exist after a session. As someone that programs in Stata, using global macros hurts my eyes, but I guess that if you use Stata only to analyse data it does little harm.
As an asside, allvar does not seem like a right name for that local macro: it does not contain all variables as it excludes the variable f. This sounds pedantic (and it is), but it is good practice to use names that accurately describe its content. In a real project we tend to come back to it after some time. A common scenarion is that you submitted a paper to a journal, it took half a year or more for the reviews to come in, and now you need to "read" your own .do-file to understand what you did half a year ago. At that point you are very happy that you were pedantic when writing the .do file...
As a further asside, assuming that a b c d e f are indeed all the variables in your dataset you can also create your local using:
ds f, not
local rhs `r(varlist)' // rhs short for right-hand side