I am trying to store the numpy.ndarrays defined as x_c, y_c, and z_c for every iteration of the loop:
for z_value in np.arange(0, 5, 1):
ms.set_current_mesh(0)
planeoffset : float = z_value
ms.compute_planar_section(planeaxis = 'Z Axis', planeoffset = planeoffset)
m = ms.current_mesh()
matrix_name = m.vertex_matrix()
x_c = matrix_name[:,0]
y_c = matrix_name[:,1]
z_c = matrix_name[:,2]
I would like to be able to recall the three arrays at any z_value, preferably with reference to the z_value i.e x_c # z_value = 2 or similar.
Thanks for any help!
p.s very new to coding, so please go easy on me.
You have to store each array in an external variable, for example a dictionary
x_c={}
y_c={}
z_c={}
for z_value in np.arange(0, 5, 1):
ms.set_current_mesh(0)
planeoffset = float(z_value)
ms.compute_planar_section(planeaxis = 'Z Axis', planeoffset = planeoffset)
m = ms.current_mesh()
m.compact()
print(m.vertex_number(), "vertices in Planar Section Z =", planeoffset)
matrix_name = m.vertex_matrix()
x_c[planeoffset] = matrix_name[:,0]
y_c[planeoffset] = matrix_name[:,1]
z_c[planeoffset] = matrix_name[:,2]
Please, ensure you call m.compact() before accessing the vertex_matrix or you will get a MissingCompactnessException error. Please, note that it is not the same to store anything in x_c[2] or in x_c[2.0], so choose if your index has to be integers o floats and keep the same type (in this example, they are floats).
Later, you can recall values like this:
print("X Values with z=2.0")
print(x_c[2.0])
Related
I want to find the value of the parameter m that minimizes my variable x subject to a system of differential equations. I have the following code
from gekko import GEKKO
def run_model_m(days, population, case, k_val, b_val, u0_val, sigma_val, Kmax0, a_val, c_val):
list_x =[]
list_u =[]
list_Kmax =[]
for i in range(len(days)):
list_xi=[]
list_ui=[]
list_Ki=[]
for j in range(len(days[i])):
#try:
m = GEKKO(remote=False)
#m.time= days[i][j]
eval = np.linspace(days[i][j][0], days[i][j][-1], 100, endpoint=True)
m.time = eval
x_data= population[i][j]
variable= np.linspace(population[i][j][0], population[i][j][-1], 100, endpoint=True)
x = m.Var(value=population[i][j][0], lb=0)
sigma= m.Param(sigma_val)
d = m.Param(c_val)
k = m.Param(k_val)
b = m.Param(b_val)
r = m.Param(a_val)
step = np.ones(len(eval))
step= 0.2*step
step[0]=1
m_param = m.CV(value=1, lb=0, ub=1, integer=True); m_param.STATUS=1
u = m.Var(value=u0_val, lb=0, ub=1)
#m.free(u)
a = m.Param(a_val)
c= m.Param(c_val)
Kmax= m.Param(Kmax0)
if case == 'case0':
m.Equations([x.dt()== x*(r*(1-x/(Kmax))-m_param/(k+b*u)-d), u.dt()== sigma*(m_param*b/((k+b*u)**2))])
elif case == 'case4':
m.Equations([x.dt()== x*(r*(1-u**2)*(1-x/(Kmax))-m_param/(k+b*u)-d), u.dt() == sigma*(-2*u*r*(1-x/(Kmax))+(b*m_param)/(b*u+k)**2)])
p = np.zeros(len(eval))
p[-1] = 1.0
final = m.Param(value=p)
m.Obj(x)
m.options.IMODE = 6
m.options.MAX_ITER=15000
m.options.SOLVER=1
# optimize
m.solve(disp=False, GUI=False)
#m.open_folder(dataset_path+'inf')
list_xi.append(x.value)
list_ui.append(u.value)
list_Ki.append(m_param.value)
list_x.append(list_xi)
list_Kmax.append(list_Ki)
list_u.append(list_ui)
return list_x, list_u, list_Kmax, m.options.OBJFCNVAL
scaled_days[i][j] =[-7.0, 42.0, 83.0, 125.0, 167.0, 217.0, 258.0, 300.0, 342.0]
scaled_pop[i][j] = [0.01762491277346285, 0.020592540360308997, 0.017870838266697213, 0.01690069378982034,0.015512320147187675,0.01506701796298272,0.014096420738841563,0.013991224004743027,0.010543380664478205]
k0,b0,group, case0, u0, sigma0, K0, a0, c0 = (100, 20, 'Size3, Inc', 'case0', 0.1, 0.05, 2, 0, 0.01)
list_x2, list_u2, list_Kmax2,final =run_model_m(days=[[scaled_days[i][j]]], population=
[[scaled_pop[i][j]]],case=case1, k_val=list_b1[i0][0], b_val=b1, u0_val=list_u1[i0][j0],
sigma_val=sigma1, Kmax0=K1, a_val=list_Kmax1[0][0], c_val=c1)
I get the error Data arrays must have the same length, and match time discretization in dynamic problems error but I don't understand why. I have tried making x and m_param arrays, with x=m.Var, m_param =m.MV... But still get the same error, even if they are all arrays of the same length. Is this the right way to find the solution of the minimization problem?
I think the error was just that in run_model_m I was passing a list as u0_val and it didn't have the same dimensions as m.time. So it should be u0_val=list_u1[0][0][0]
How can I collapse the values of "deep" struct fields into arrays by just indexing?
In the example below, I can only do it for the "top-most" level, and for "deeper" levels I get the error:
"Expected one output from a curly brace or dot indexing expression, but there were XXX results."
The only workaround I found so far is to unfold the operation into several steps, but the deeper the structure the uglier this gets...
clc; clear variables;
% Dummy data
my_struc.points(1).fieldA = 100;
my_struc.points(2).fieldA = 200;
my_struc.points(3).fieldA = 300;
my_struc.points(1).fieldB.subfieldM = 10;
my_struc.points(2).fieldB.subfieldM = 20;
my_struc.points(3).fieldB.subfieldM = 30;
my_struc.points(1).fieldC.subfieldN.subsubfieldZ = 1;
my_struc.points(2).fieldC.subfieldN.subsubfieldZ = 2;
my_struc.points(3).fieldC.subfieldN.subsubfieldZ = 3;
my_struc.info = 'Note my_struc has other fields besides "points"';
% Get all fieldA values by just indexing (this works):
all_fieldA_values = [my_struc.points(:).fieldA]
% Get all subfieldM values by just indexing (doesn't work):
% all_subfieldM_values = [my_struc.points(:).fieldB.subfieldM]
% Ugly workaround:
temp_array_of_structs = [my_struc.points(:).fieldB];
all_subfieldM_values = [temp_array_of_structs.subfieldM]
% Get all subsubfieldZ values by just indexing (doesn't work):
% all_subsubfieldZ_values = [my_struc.points(:).fieldC.subfieldN.subsubfieldZ]
% Ugly workaround:
temp_array_of_structs1 = [my_struc.points(:).fieldC];
temp_array_of_structs2 = [temp_array_of_structs1.subfieldN];
all_subsubfieldZ_values = [temp_array_of_structs2.subsubfieldZ]
Output:
all_fieldA_values =
100 200 300
all_subfieldM_values =
10 20 30
all_subsubfieldZ_values =
1 2 3
Thanks for any help!
You can use arrayfun to have acces to each individual 'point', and then acces its data. This will return an array with the same dimensions as my_struc.points:
all_subfieldM_values = arrayfun(#(in) in.fieldB.subfieldM, my_struc.points)
all_subsubfieldZ_values = arrayfun(#(in) in.fieldC.subfieldN.subsubfieldZ, my_struc.points)
Not optimal, but at least it's one line.
I have (in a larger project) data contained in numpy.array.
Based on user input I need to move a selected axis (dimAxisNr) to the first dimension of the array and slice one or more (including the first) dimension based on user input (such as Select2 and Select0 in the example).
Using this input I generate a DataSelect which contains the information needed to slice. But the output size of the sliced array is different from the one using inline indexing. So basically I need a way to generate the '37:40:2' and '0:2' from an input list.
import numpy as np
dimAxisNr = 1
Select2 = [37,39]
Select0 = [0,1]
plotData = np.random.random((102,72,145,2))
DataSetSize = np.shape(plotData)
DataSelect = [slice(0,item) for item in DataSetSize]
DataSelect[2] = np.array(Select2)
DataSelect[0] = np.array(Select0)
def shift(seq, n):
n = n % len(seq)
return seq[n:] + seq[:n]
#Sort and Slice the data
print(np.shape(plotData))
print(DataSelect)
plotData = np.transpose(plotData, np.roll(range(plotData.ndim),-dimAxisNr))
DataSelect = shift(DataSelect,dimAxisNr)
print(DataSelect)
print(np.shape(plotData))
plotData = plotData[DataSelect]
print(np.shape(plotData))
plotDataDirect = plotData[slice(0, 72, None), 37:40:2, slice(0, 2, None), 0:2]
print(np.shape(plotDataDirect))
I'm not sure I've understood your question at all...
But if the question is "How do I generate a slice based on a list of indices like [37,39,40,23] ?"
then I would answer : you don't have to, just use the list as is to select the right indices, like so :
a = np.random.rand(4,5)
print(a)
indices = [2,3,1]
print(a[0:2,indices])
Note that the sorting of the list matters: [2,3,1] yields a different result from [1,2,3]
Output :
>>> a
array([[ 0.47814802, 0.42069094, 0.96244966, 0.23886243, 0.86159478],
[ 0.09248812, 0.85569145, 0.63619014, 0.65814667, 0.45387509],
[ 0.25933109, 0.84525826, 0.31608609, 0.99326598, 0.40698516],
[ 0.20685221, 0.1415642 , 0.21723372, 0.62213483, 0.28025124]])
>>> a[0:2,[2,3,1]]
array([[ 0.96244966, 0.23886243, 0.42069094],
[ 0.63619014, 0.65814667, 0.85569145]])
I have found the answer to my question. I need to use numpy.ix_.
Here is the working code:
import numpy as np
dimAxisNr = 1
Select2 = [37,39]
Select0 = [0,1]
plotData = np.random.random((102,72,145,2))
DataSetSize = np.shape(plotData)
DataSelect = [np.arange(0,item) for item in DataSetSize]
DataSelect[2] = Select2
DataSelect[0] = Select0
#print(list(37:40:2))
def shift(seq, n):
n = n % len(seq)
return seq[n:] + seq[:n]
#Sort and Slice the data
print(np.shape(plotData))
print(DataSelect)
plotData = np.transpose(plotData, np.roll(range(plotData.ndim),-dimAxisNr))
DataSelect = shift(DataSelect,dimAxisNr)
plotDataSlice = plotData[np.ix_(*DataSelect)]
print(np.shape(plotDataSlice))
plotDataDirect = plotData[slice(0, 72, None), 37:40:2, slice(0, 2, None), 0:1]
print(np.shape(plotDataDirect))
The code I'm using imports data from multiple files and saves them into an array of cells, the code is as follows:
[FileName,PathName,FilterIndex] = uigetfile('*.txt*','MultiSelect','on');
numfiles = size(FileName,2);
FileData= cell(1,numfiles);
for ii = 1:numfiles
FileName{ii};
A=[];
entirefile =fullfile(PathName,FileName{ii});
fid = fopen(entirefile);
tline = fgets(fid);
while ischar(tline)
parts = textscan(tline, '%f;');
if numel(parts{1}) > 0
A = [ A ; parts{:}' ];
end
tline = fgets(fid);
end
fclose(fid);
FileData{ii} = A;
A = FileData{ii};
X = A(:,1);
Y = A(:,5);
DataToUse = [X,Y];
end
Now my issue is I want to use the first DataToUse created by the loop, which will be data from the first file, seperatley to the other files but I can not issolate it. I have tried DataToUse(1), DataToUse(1,1) and DataToUse(:,[1,2]) but none are working for me. An example of the type of data would be:
DataToUse=
0.0762 0.0271
0.0763 0.2671
0.0764 0.4079
0.0765 0.0510
0.0766 0.0087
0.0767 0.0099
0.0768 0.0067
0.0769 0.0047
0.0770 0.0047
0.0771 0.0349
0.0772 0.2094
0.0773 0.2740
0.0774 0.0294
0.0775 0.0100
0.0776 0.0159
I have different numbers of this kind of data depending on how many files are selected but I would like to only use the first initially and use the others later. Anybody know how I can go about doing this? Many thanks in advance
The solution is to use cell arrays, like so:
DataToUse{ii} = [X, Y]
To get the desired output put this after your for-loop:
firstLoopXY = DataToUse{1}
Enjoy!
This is a strange question but it baffles me. I want to be able to store x and y co-ords on an id based system such as: id.1.x = 10, id.1.y = 15: id.2.x = 50, id.2.y = 42 and I am trying to make a function to do it for me, I am having issues. here's my code
a = { p = {x,y}}
function box(xpos,ypos,id)
a[id].x = xpos
a[id].y = ypos
end
box(25,30,1)
box(45,10,2)
print(a[1].x.." "..a[1].y)
print(a[2].x.." "..a[2].y)
which I wanted to print:
25 30
45 10
but instead I get the error:
attempt to index global '?' (a nil value)
I am really exhausted and would like to put this to rest so if somebody could help it would be greatly appreciated.
function box(xpos,ypos,id)
a[id] = {x = xpos, y = ypos}
end