Develop Reference

How to assign a 2d libreoffice calc named range to a python variable. Can do it in Libreoffice Basic

I can't seem to find a simple answer to the question. I have this successfully working in Libreoffice Basic:
NamedRange = ThisComponent.NamedRanges.getByName("transactions_detail")
RefCells = NamedRange.getReferredCells()
Set MainRange = RefCells.getDataArray()
Then I iterate over MainRange and pull out the rows I am interested in.
Can I do something similar in a python macro? Can I assign a 2d named range to a python variable or do I have to iterate over the range to assign the individual cells?
I am new to python but hope to convert my iteration intensive macro function to python in hopes of making it faster.
Any help would be much appreciated.
Thanks.

LibreOffice can be manipulated from Python with the library pyuno. The documentation of pyuno is unfortunately incomplete but going through this tutorial may help.
To get started:
Python-Uno, the library to communicate via Uno, is already in the LibreOffice Python’s path. To initialize your context, type the following lines in your python shell :
import socket # only needed on win32-OOo3.0.0
import uno
# get the uno component context from the PyUNO runtime
localContext = uno.getComponentContext()
# create the UnoUrlResolver
resolver = localContext.ServiceManager.createInstanceWithContext(
"com.sun.star.bridge.UnoUrlResolver", localContext )
# connect to the running office
ctx = resolver.resolve( "uno:socket,host=localhost,port=2002;urp;StarOffice.ComponentContext" )
smgr = ctx.ServiceManager
# get the central desktop object
desktop = smgr.createInstanceWithContext( "com.sun.star.frame.Desktop",ctx)
# access the current writer document
model = desktop.getCurrentComponent()
Then to get a named range and access the data as an array, you can use the following methods:
NamedRange = model.NamedRanges.getByName(“Test Name”)
MainRange = NamedRange.getDataArray()
However I am unsure that this will result in a noticeable preformance gain.

Getting a selection in 3ds Max into a list in Python

I am writing in Python, sometimes calling certain aspects of maxscript and I have gotten most of the basics to work. However, I still don't understand FPValues. I don't even understand while looking through the examples and the max help site how to get anything meaningful out of them. For example:
import MaxPlus as MP
import pymxs
MPEval = MP.Core.EvalMAXScript
objectList = []
def addBtnCheck():
select = MPEval('''GetCurrentSelection()''')
objectList.append(select)
print(objectList)
MPEval('''
try (destroyDialog unnamedRollout) catch()
rollout unnamedRollout "Centered" width:262 height:350
(
button 'addBtn' "Add Selection to List" pos:[16,24] width:88 height:38
align:#left
on 'addBtn' pressed do
(
python.Execute "addBtnCheck()"
)
)
''')
MP.Core.EvalMAXScript('''createDialog unnamedRollout''')
(I hope I got the indentation right, pretty new at this)
In the above code I successfully spawned my rollout, and used a button press to call a python function and then I try to put the selection of a group of objects in a variable that I can control through python.
The objectList print gives me this:
[<MaxPlus.FPValue; proxy of <Swig Object of type 'Autodesk::Max::FPValue *' at 0x00000000846E5F00> >]
When used on a selection of two objects. While I would like the object names, their positions, etc!
If anybody can point me in the right direction, or explain FPValues and how to use them like I am an actual five year old, I would be eternally grateful!

Where to start, to me the main issue seems to be the way you're approaching it:
why use MaxPlus at all, that's an low-level SDK wrapper as unpythonic (and incomplete) as it gets
why call maxscript from python for things that can be done in python (getCurrentSelection)
why use maxscript to create UI, you're in python, use pySide
if you can do it in maxscript, why would you do it in python in the first place? Aside from faster math ops, most of the scene operations will be orders of magnitude slower in python. And if you want, you can import and use python modules in maxscript, too.

import MaxPlus as MP
import pymxs
mySel = mp.SelectionManager.Nodes
objectList = []
for each in mySel:
x = each.Name
objectList.append(x)
print objectList

The easiest way I know is with the
my_selection = rt.selection
command...
However, I've found it works a little better for me to throw it into a list() function as well so I can get it as a Python list instead of a MAXscript array. This isn't required but some things get weird when using the default return from rt.selection.
my_selection = list(rt.selection)
Once you have the objects in a list you can just access its attributes by looking up what its called for MAXscript.
for obj in my_selection:
print(obj.name)

Error calling modules when using x = _import_("y") with dictionary values

I'm having trouble understanding an issue with calling packages after they have been imported using the __import_ function in Python. I'll note that using the usual import x as y works fine, but this is an learning exercise for me. I am importing and then checking the version for multiple packages and to learn a little more about Python, I wanted to automate this by using a dictionary.
My dictionary looks something like this:
pack = {"numpy": ["np", "1.7.1"]}
and I then use this to load and check the modules:
for keys in pack.keys():
pack[keys][0] = __import__(keys)
print("%s version: %6.6s (need at least %s)" %(keys, pack[keys][0].__version__, pack[keys][1]))
This works fine, but when I later try to call the package, it does not recognize it: x = np.linspace(0,10,30)
produces an error saying np isn't recognized, but this works: x = pack[keys][0].linspace(0,10,30)
Since this is just a way for me to learn, I'd also be interested in any solutions that change how I've approached the problem. I went with dictionaries because I've at least heard of them, and I used the _import__ function since I was forced to either use quoted characters or numeric values in my dictionary values. The quoted characters created problems for the import x as y technique.

Althought it isn't a good practice, you can create variables dynamically using the builtin locals function.
So,
for module, data in pack.items():
locals()[data[0]] = __import__(module)
nickname = locals()[data[0]]
print("%s version: %6.6s (need at least %s)" %(module, nickname.__version__, pack[module][1]))
Output:
numpy version: 1.12.1 (need at least 1.7.1)

Multiple return types from single scipy function

I'm using the scipy library for calculating t-tests and I'm trying to understand why there would be multiple return types from a single function call. It caught me out, as in different environments, it returns different objects.
For instance, locally I get:
rvs1 = stats.norm.rvs(loc=5,scale=10,size=500)
rvs2 = stats.norm.rvs(loc=5,scale=10,size=500)
stats.ttest_ind(rvs1,rvs2)
(0.26833823296239279, 0.78849443369564776)
stats.ttest_ind(rvs1,rvs2, equal_var = False)
(0.26833823296239279, 0.78849452749500748)
which is what it shows on the example on their website. However, when it runs on CircleCI (a continuous integration service) in a virtual machine I get:
rvs1 = stats.norm.rvs(loc=5,scale=10,size=500)
rvs2 = stats.norm.rvs(loc=5,scale=10,size=500)
stats.ttest_ind(rvs1,rvs2)
(array(-1.603223040889008), 0.10920183417176929)
stats.ttest_ind(rvs1,rvs2, equal_var = False)
(array(-1.603223040889008), 0.10920185489799822)
where the first element of the tuple is now an array!
Why have these different return types that seem almost interchangeable? Is it for backwards compatibility (although both server and locally were running 0.14.0)? How could the environment have this effect?
I eventual solved it by running:
np.array(stats.ttest_ind(rvs1,rvs2))
which converted it into a single array of two elements.
As interested as I am in a better solution to this issue, I'd like to get to the bottom of why there are different return types from the single function and why it was designed as such?

To always ensure I had a consistent output to this, I wrapped it in a numpy array and flattened it:
np.array(stats.ttest_ind(rvs1.values, rvs2.values)).flatten()
which will return:
[0.26833823296239279, 0.78849443369564776]
regardless of the version

Python c_types .dll functions (pari library)

Alright, so a couple days ago I decided to try and write a primitive wrapper for the PARI library. Ever since then I've been playing with ctypes library in loading the dll and accessing the functions contained using code similar to the following:
from ctypes import *
libcyg=CDLL("<path/cygwin1.dll") #It needs cygwin to be loaded. Not sure why.
pari=CDLL("<path>/libpari-gmp-2.4.dll")
print pari.fibo #fibonacci function
#prints something like "<_FuncPtr object at 0x00BA5828>"
So the functions are there and they can potentially be accessed, but I always receive an access violation no matter what I try. For example:
pari.fibo(5) #access violation
pari.fibo(c_int(5)) #access violation
pari.fibo.argtypes = [c_long] #setting arguments manually
pari.fibo.restype = long #set the return type
pari.fibo(byref(c_int(5))) #access violation reading 0x04 consistently
and any variation on that, including setting argtypes to receive pointers.
The Pari .dll is written in C and the fibonacci function's syntax within the library is GEN fibo(long x).
Could it be the return type that's causing these errors, as it is not a standard int or long but a GEN type, which is unique to the PARI library? Any help would be appreciated. If anyone is able to successfully load the library and use ANY function from within python, please tell; I've been at this for hours now.
EDIT: Seems as though I was simply forgetting to initialize the library. After a quick pari.pari_init(4000000,500000) it stopped erroring. Now my problem lies in the in the fact that it returns a GEN object; which is fine, but whenever I try to reference the address to which it points, it's always 33554435, which I presume is still an address. I'm trying further commands and I'll update if I succeed in getting the correct value of something.

You have two problems here, one give fibo the correct return type and two convert the GEN return type to the value you are looking for.
Poking around the source code a bit, you'll find that GEN is defined as a pointer to a long. Also, at looks like the library provides some converting/printing GENs. I focused in on GENtostr since it would probably be safer for all the pari functions.
import cytpes
pari = ctypes.CDLL("./libpari.so.2.3.5") #I did this under linux
pari.fibo.restype = ctypes.POINTER(ctypes.c_long)
pari.GENtostr.restype = ctypes.POINTER(ctypes.c_char)
pari.pari_init(4000000,500000)
x = pari.fibo(100)
y = pari.GENtostr(x)
ctypes.string_at(y)
Results in:
'354224848179261915075'