Suppose I have numerous number of outputs and I want them to show as follow
Friction factor = xxx
Load factor = xxx
Thermal factor = xxxx
Is there any way to make the equal sign '=' align to each other? I've tried using the 'fprintf' function with '\t'. However, it's tough for me to achieve such arrangement.
Sincerely thank you for all the helps.
You could do the following:
names = {'Friction Factor','Load Factor','Thermal Factor'};
values = [xx,yy,zz];
nameLength = cellfun(#numel,names);
format = sprintf('%%-%is = %%f\\n',max(nameLength));
for n = 1:length(names)
fprintf(format,names{n},values(n));
end
What about this:
disp(['Friction factor = ' num2str(xxx)])
disp(['Load factor = ' num2str(yyy)])
disp(['Thermal factor = ' num2str(zzz)])
Related
When trying to create a table with the conditional random effects in r using the gtsummary function tbl_regression from a glmmTMB mixed effects negative-binomial zero-inflated model, I get duplicate random effects rows.
Example (using Mollie Brooks' Zero-Inflated GLMMs on Salamanders Dataset):
data(Salamanders)
head(Salamanders)
library(glmmTMB)
zinbm2 = glmmTMB(count~spp + mined +(1|site), zi=~spp + mined + (1|site), Salamanders, family=nbinom2)
zinbm2_table_cond <- tbl_regression(
zinbm2,
tidy_fun = function(...) broom.mixed::tidy(..., component = "cond"),
exponentiate = TRUE,
estimate_fun = purrr::partial(style_ratio, digits = 3),
pvalue_fun = purrr::partial(style_sigfig, digits = 3))
zinbm2_table_cond
Output:
Random Effects Output (cond)
When extracting the random effects from de zero-inflated part of the model I get the same problem.
Example:
zinbm2_table_zi <- tbl_regression(
zinbm2,
tidy_fun = function(...) broom.mixed::tidy(..., component = "zi"),
exponentiate = TRUE,
estimate_fun = purrr::partial(style_ratio, digits = 3),
pvalue_fun = purrr::partial(style_sigfig, digits = 3))
zinbm2_table_zi
Output:
Random Effects Output (zi)
The problem persists if I specify the effects argument in broom.mixed.
tidy_fun = function(...) broom.mixed::tidy(..., effects = "ran_pars", component = "cond"),
Looking at confidence intervals in both outputs it seems that somehow it is extracting random effects from both parts of the model and changing the estimate of the zero-inflated random effects (in 1st image; opposite in the 2nd image) to match the conditional part estimate while keeping the CI.
I am not knowledgeable enough to understand why this is happening. Since both rows have the same label I am having difficulty removing the wrong one.
Any tips on how to avoid this problem or a workaround to remove the undesired rows?
If you need more info, let me know.
Thank you in advance.
PS: Output images were changed to link due to insufficient reputation.
I am trying in the code below to generate a report with side by side two images and a splited table but I get an error. Why this error occur?
Code:
close all;
clear all;
clc;
import mlreportgen.report.*
import mlreportgen.dom.*
import mlreportgen.utils.*
Name = {'A';'B';'C';'D';'E';'A';'B';'C';'D';'E'};
codeA = [8;3;8;0;4;8;3;8;0;4];
Height = [1;8;4;7;8;8;3;1;0;4];
Weight = [6;2;1;4;5;8;3;1;1;4];
T = table(Name,codeA,Height,Weight,codeA,Height,Weight,codeA,Height,Weight);
Image1 = Image(which('coins.png'));
Image2 = Image(which('sevilla.jpg'));
rpt = Report("myPDF","pdf");
imgStyle = {ScaleToFit(true)};
Image2.Style = imgStyle;
Image1.Style = imgStyle;
lot = Table({Image2, ' ', Image1});
lot.entry(1,1).Style = {Width('3.2in'), Height('3in')};
lot.entry(1,2).Style = {Width('.2in'), Height('3in')};
lot.entry(1,3).Style = {Width('3.2in'), Height('3in')};
lot.Style = {ResizeToFitContents(false), Width('100%')};
add(rpt, lot);
chapter = Chapter("Title",'Table Report');
table = FormalTable(T);
table.Border = 'Solid';
table.RowSep = 'Solid';
table.ColSep = 'Solid';
para = Paragraph(['The table is sliced into two tables, '...
'with the first column repeating in each table.']);
para.Style = {OuterMargin('0in','0in','0in','12pt')};
para.FontSize = '14pt';
add(chapter,para)
slicer = TableSlicer("Table",table,"MaxCols",5,"RepeatCols",1);
totcols = slicer.MaxCols - slicer.RepeatCols;
slices = slicer.slice();
for slice=slices
str = sprintf('%d repeating column and up to %d more columns',...
slicer.RepeatCols,totcols);
para = Paragraph(str);
para.Bold = true;
add(chapter,para)
add(chapter,slice.Table)
end
add(rpt,chapter)
close(rpt)
rptview(rpt)
Error:
*Index exceeds the number of array elements. Index must not exceed 10.
Error in try1 (line 26)
lot.entry(1,1).Style = {Width('3.2in'), Height('3in')};*
You define the variable
Height = [1;8;4;7;8;8;3;1;0;4];
Then you try and use the report gen function Height
lot.entry(1,1).Style = {Width('3.2in'), Height('3in')};
Because you've shadowed the Height function with a variable, MATLAB is trying to get the element of this array at index '3in', which is either nonsensical or (via some implicit ASCII conversion) is way out of range.
Per my comment on your previous question, I think the way the documentation suggests the report gen functions are imported is bad practice. By using import mlreportgen.dom.* you are putting all of the nicely name-spaced functions from that package into the common area, and in this case it has caused an unclear clash between two things. So there are two options:
Use the namespaced version of Height (and Width), if you did this with all of the report gen functions you would not need the import. The nice side-effect is you get tab-completion when typing the various functions from this package
lot.entry(1,1).Style = {mlreportgen.dom.Width('3.2in'), mlreportgen.dom.Height('3in')};
Sure, you code is longer, but it is more explicit.
... or ...
Simply don't define a variable called Height. Rename this and everything else can stay the same.
I'm a beginner in Python and I'm working with the Ansys Customization Tool (ACT) to add my own extension.
Is there a direct way to fill a file with every node's coordinates after deformation?
hopefully in 3 lines or columns: x , y , z
So far I only found the GetNodeValue object but it only gives me the displacement and I need the deformed coordinates for the entire model.
My first idea was to add the displacements to the initial coordinates but I didn't manage to do it.
Many thanks for your help
Lara
APDL Snippet
Add an APDL Snippet in the solution part of the tree:
/prep7
UPGEOM,1,1,1,file,rst ! adds the displacements to the nodal coordinates.
cdwrite,geom,nodesAndelements,geom ! Writes node and element data to nodesAndelement.geom
I'm not sure if you can work with the output format from cdwrite, but this is the quickest solution i can think of.
If you want to automate you have to insert the command snippet via
solution = ExtAPI.DataModel.Project.Model.Analyses[0].Solution
fullPath = "path//to//snippet"
snippet = solution.AddCommandSnippet()
snippet.ImportTextFile(fullPath)
ACT
If you want to stay in ACT it could be done like this:
global nodeResults
import units
analysis = ExtAPI.DataModel.Project.Model.Analyses[0]
mesh = analysis.MeshData
# Get nodes
allNodes = mesh.Nodes
# get the result data
reader = analysis.GetResultsData()
# get the deformation result
myDeformation = reader.GetResult("U")
nodeResultsTemp = []
result_unit = myDeformation.GetComponentInfo("X").Unit
for node in allNodes:
# get node deformation and convert values in meter
deformationNode1 = myDeformation.GetNodeValues(node.Id)
deformationNode1[0] = units.ConvertUnit(deformationNode1[0],result_unit,"m","Length")
deformationNode1[1] = units.ConvertUnit(deformationNode1[1],result_unit,"m","Length")
deformationNode1[2] = units.ConvertUnit(deformationNode1[2],result_unit,"m","Length")
# add node coordinates (in meter) to the displacement
mesh_unit = mesh.Unit
node1 = mesh.NodeById(node.Id)
node1CoorX = units.ConvertUnit(node1.X,mesh_unit,"m","Length")
node1CoorY = units.ConvertUnit(node1.Y,mesh_unit,"m","Length")
node1CoorZ = units.ConvertUnit(node1.Z,mesh_unit,"m","Length")
deformationNode1[0] = deformationNode1[0]+node1CoorX
deformationNode1[1] = deformationNode1[1]+node1CoorY
deformationNode1[2] = deformationNode1[2]+node1CoorZ
nodeResultsTemp.append([node1.X,node1.Y,node1.Z,deformationNode1[0],deformationNode1[1],deformationNode1[2]])
nodeResults = nodeResultsTemp
So I have a list of stars and their respective distances. My assignment is to find which stars are in a certain distance (+- 10parsec). I want to exclude some of them from further calculations in the program. The thing is I don't want to remove them completely so remove, pop etc isn't helping me. I still want those stars on the list to be present in my output csv. I just want a line saying something like those stars which don't support the if statement, don't use them in this calculation. So i guess the output would be blank for those.
I suppose it is an if or for statement, to mark those bad stars as False and then down the line use calculation that excludes those faulty stars.
I'm a physics student and this is my first python program ever! Please be cool about my ignorance...
Edit: forgive me if i include useless stuff i don't really know what's important. I also use uncertainties library if its of any use
column_names = ['id','pi','s_pi','v_r' ,'s_v', 'dis', 'X',
'ra_h', 'ra_m', 'ra_s','dec_d', 'dec_m',
'dec_s', 'ma', 's_ma', 'md', 's_md']
data = pd.read_csv("hyades_data.dat", skiprows=2, sep='\s+',
names=column_names)
calculations with all
v_r = unumpy.uarray(data['v_r'], data['s_v'])
ma = unumpy.uarray(data['ma'], data['s_ma'])
md = unumpy.uarray(data['md'], data['s_md'])
mi = unumpy.sqrt(ma**2+md**2)
r_m = v_r*unumpy.tan(th)/(4.74*mi/1000)
diff = np.abs(r_pc - r_m)
'''
if np.abs(dist-46.43) <=10:
r_m=True
else r_m=False
at this point i want to make the distiction
'''
mean_diff = diff.mean()
print("Mean : ")
print(mean_diff)
print(a_ref,d_ref)
df_va=pd.DataFrame(v_r)
df_mi = pd.DataFrame(mi)
df_rm = pd.DataFrame(r_m)
df_rpc = pd.DataFrame(r_pc)
df_diff = pd.DataFrame(diff)
#df_mean_diff = pd.DataFrame(mean_diff)
ve = v_r*np.tan(th)
output = pd.concat([data['id'], ra, dec, th_d, df_mi, df_rm, df_rpc,
df_diff,df_va], axis=1)
output.columns = ['id','ra', 'dec', 'th_d','mi', 'r_m', 'r_pc',
'dist_diff','va']
output.to_csv('results.csv', index=False)
I am trying to estimate a SUR model of the form
y_{1,t} = \alpha_1 +\beta_1 x_{1,t} + \beta_2 x_{2,t} + \beta_3 y_{1,t-1} +\epsilon_{1,t}
y_{2,t} = \alpha_2 +\beta_4 x_{1,t} + \beta_5 x_{2,t} + \beta_6 y_{2,t-1} +\epsilon_{2,t}
Define mY = [y_1 y_2] and mX = [x_1 x_2].
For this purpose I am doing
iT = size(mY,1); iN = size(mY,2);
mXsur = kron(mX, eye(iN));
mXsurCell = mat2cell(mXsur, iN*ones(iT,1));
iR = size(mXsur,2);
Mdl = vgxset('n', iN, 'nAR',1, 'nX',iR,'Constant',true);
[SurOutput, SurSDerror, ~,SURcov] = vgxvarx(Mdl, mY, mXsurCell);
The issue is that the bit of code nAR, 1 seems to add 1 lag of both y variables to each equation and I only wish to add one per equation. Is there a quick way to do this?
(Of course I can include the lagged terms manually in the mX matrix, but my question is whether we can do this via vgxset in a quicker way. I think not based on my reading of the help file, but still want to double check). Thanks