I am trying to solve a nonlinear program with Direct collocation in drake. I want to regain the solution when SNOPT solved the program successfully. First I saved the initial guess of each variable in a .txt file, then I read the initial guess and set decision variables by SetInitialGuess() , and change nothing else, but I did not get the same solution, WHY?
MOREOVER, when I run more times, the latter solutions are same.
e.g.
solution1 != solution2; solution2 == solution3; solution3 == solution4;.....
I have check each initial guess to make sure they are same. Is there options in SNOPT or initial settings in NP should be set beside the initial guess of decision variables to get a same solution?
Without knowing more about your program, one possible reason is that when you save the initial guess to a.txt, the floating number get truncated when print it to a txt file. So in the second run, the initial guess is not exactly the same as the first run. And this tiny difference in the initial guess causes the SNOPT to find a different solution. For solution 2, 3, 4, do they all load the initial guess from a.txt?
In order to print the floating number to the txt file, you could use setprecision command.
I have last version of jupyterlab Version 3.0.14
in debugging mode many variables not needed exists in the right box
what are they?
I want to delete them except i, j and result.
You can adjust the variableFilters setting of the debugger to hide variable that you are not interested in; see this answer for more details.
The debugger simply shows all variables that are available at runtime in the kernel; the Python kernels (IPython and Xeus Python) come with a feature of remembering your input and output for each executed cell; it is immensely useful if you execute a cell with compute-intensive task but forget to assign the result onto a variable, for example instead of:
result = do_long_calculation()
you do:
do_long_calculation()
in the latter case you can use the IPython underscore _ variable which caches the last execution result to recover the output:
result = _
If you already overwritten the most recent output, it the previous one goes to __ and so on. Learn more about this in the output caching system documentation.
Similarly _i, _ii, etc. variables cache most recent input of cells so you can check what has been executed. See more in input caching system documentation. There are also In and Out variables which store entire execution history for your reference.
Fur the future convenience I raised the idea of allowing regular expressions to hide all variables matching a pattern here.
I've created a model with Uppaal in which several integer variables change over the course of time. Now I would like to save the values of the variables during the modelling process somewhere (best in xml or a text file). In the Uppaal documentation (https://www.it.uu.se/research/group/darts/uppaal/documentation.shtml) I found the method in point 13 (How do I export and interpret the traces from Uppaal?) and tried the Java API way already, in the hope that it can output the variables as well as the traces. Unfortunately this method seems to be limited to traces. Does anyone know a method to save the variable values from Uppaal?
Hopeful greetings,
Josi
Solution from the comments.
to export the variable value tractory over time, one may use SMC query in the verifier.
For example:
Typeset the following query: simulate 1 [<=300] { Gate.len }
Click Check
Right-click on the query, and from the popup menu choose Simulations (1)
Observe a new window popup with a plot
Right-click on the plot and choose Export Comma Separated Values
Follow the save file dialog and observe the resulting file to contain time and value sequence.
Note that SMC assumes that all channels are broadcast and there are no deadlocks.
I'm having trouble reading an unformatted F77 binary file in Python.
I've tried the SciPy.io.FortraFile method and the NumPy.fromfile method, both to no avail. I have also read the file in IDL, which works, so I have a benchmark for what the data should look like. I'm hoping that someone can point out a silly mistake on my part -- there's nothing better than having an idiot moment and then washing your hands of it...
The data, bcube1, have dimensions 101x101x101x3, and is r*8 type. There are 3090903 entries in total. They are written using the following statement (not my code, copied from source).
open (unit=21, file=bendnm, status='new'
. ,form='unformatted')
write (21) bcube1
close (unit=21)
I can successfully read it in IDL using the following (also not my code, copied from colleague):
bcube=dblarr(101,101,101,3)
openr,lun,'bcube.0000000',/get_lun,/f77_unformatted,/swap_if_little_endian
readu,lun,bcube
free_lun,lun
The returned data (bcube) is double precision, with dimensions 101x101x101x3, so the header information for the file is aware of its dimensions (not flattend).
Now I try to get the same effect using Python, but no luck. I've tried the following methods.
In [30]: f = scipy.io.FortranFile('bcube.0000000', header_dtype='uint32')
In [31]: b = f.read_record(dtype='float64')
which returns the error Size obtained (3092150529) is not a multiple of the dtypes given (8). Changing the dtype changes the size obtained but it remains indivisible by 8.
Alternately, using fromfile results in no errors but returns one more value that is in the array (a footer perhaps?) and the individual array values are wildly wrong (should all be of order unity).
In [38]: f = np.fromfile('bcube.0000000')
In [39]: f.shape
Out[39]: (3090904,)
In [42]: f
Out[42]: array([ -3.09179121e-030, 4.97284231e-020, -1.06514594e+299, ...,
8.97359707e-029, 6.79921640e-316, -1.79102266e-037])
I've tried using byteswap to see if this makes the floating point values more reasonable but it does not.
It seems to me that the np.fromfile method is very close to working but there must be something wrong with the way it's reading the header information. Can anyone suggest how I can figure out what should be in the header file that allows IDL to know about the array dimensions and datatype? Is there a way to pass header information to fromfile so that it knows how to treat the leading entry?
I played a bit around with it, and I think I have an idea.
How Fortran stores unformatted data is not standardized, so you have to play a bit around with it, but you need three pieces of information:
The Format of the data. You suggest that is 64-bit reals, or 'f8' in python.
The type of the header. That is an unsigned integer, but you need the length in bytes. If unsure, try 4.
The header usually stores the length of the record in bytes, and is repeated at the end.
Then again, it is not standardized, so no guarantees.
The endianness, little or big.
Technically for both header and values, but I assume they're the same.
Python defaults to little endian, so if that were the the correct setting for your data, I think you would have already solved it.
When you open the file with scipy.io.FortranFile, you need to give the data type of the header. So if the data is stored big_endian, and you have a 4-byte unsigned integer header, you need this:
from scipy.io import FortranFile
ff = FortranFile('data.dat', 'r', '>u4')
When you read the data, you need the data type of the values. Again, assuming big_endian, you want type >f8:
vals = ff.read_reals('>f8')
Look here for a description of the syntax of the data type.
If you have control over the program that writes the data, I strongly suggest you write them into data streams, which can be more easily read by Python.
Fortran has record demarcations which are poorly documented, even in binary files.
So every write to an unformatted file:
integer*4 Test1
real*4 Matrix(3,3)
open(78,format='unformatted')
write(78) Test1
write(78) Matrix
close(78)
Should ultimately be padded by an np.int32 values. (I've seen references that this tells you the record length, but haven't verified persconally.)
The above could be read in Python via numpy as:
input_file = open(file_location,'rb')
datum = np.dtype([('P1',np.int32),('Test1',np.int32),('P2',np.int32),('P3',mp.int32),('MatrixT',(np.float32,(3,3))),('P4',np.int32)])
data = np.fromfile(input_file,datum)
Which should fully populate the data array with the individual data sets of the format above. Do note that numpy expects data to be packed in C format (row major) while Fortran format data is column major. For square matrix shapes like that above, this means getting the data out of the matrix requires a transpose as well, before using. For non square matrices, you will need to reshape and transpose:
Matrix = np.transpose(data[0]['MatrixT']
Transposing your 4-D data structure is going to need to be done carefully. You might look into SciPy for automated ways to do so; the SciPy package seems to have Fortran related utilities which I have not fully explored.
Hi all, I'm *very* new at the whole programming thing, but I really like it. Sorry if I do not have enough details
So pretty much, I have excel files that have columns with numbers ( I can't post a picture because I don't have 10 reputations yet.
I've been searching for a couple days and I haven't really found an answer to this. I was wondering if there is a way that I could either create multiple arrays from the file - I know we can't make matrices like matlab - that have the numbers in each column, i.e.
float numbers[] = {1.3, 1.2, 4.2};
Or create the excel file with numbers (the iWork version of excel), and import the numbers file into the xcode project and from there create the arrays
The issue I have is that there's around a thousand numbers so copying it one by one is extremely time consuming
sorry if this is confusing, please let me know if there's anything else I should add as information
There's two easy ways to do this, even if you are a beginner.
Open MS Excel, input your values, save excel document as .CSV file.
Then grab an objective-c CSV converter like this one here in github and you are done.
The second way, you could declare an array of numbers (in example, 'float matrix[5][5];') and use it however you see fit.
I've done both of my suggestions in seperate projects and both work very well. I used the first method for a 15 page excel document that I needed to use in my app, the second method I used in another app that I needed to constantly change the contents of the 2d array.
Once you declare the 'float matrix[5][5]' this is a 5x5 matrix (a.k.a. table) you can use it however you want. You could have the first array be the column and the second array be the row.
You mean generate objective-C source from a .csv file? One way to do this would be to use a scripting language like Perl:
#!/usr/bin/perl
use warnings;
use strict;
my #numbers = ();
while (<>) {
chomp;
push #numbers, $_;
}
my $numbers = join(', ', #numbers);
print qq(float numbers[] = {$numbers};\n);
This assumes that your .csv file (say foo.csv) has numbers in the first column and nothing else. If the file contains:
1.5
2.5
18842984
-4
And you pipe it to the script (foo.pl in this example):
cat foo.csv | perl foo.pl
It will output this:
float numbers[] = {1.5, 2.5, 18842984, -4};
Is that what you're trying to do?