Hey,
I have a file:
;Eintrittsdatum;;;
;04.03.16;;10,00 €;genehmigt
;04.03.16;;10,00 €;genehmigt
which I try to parse using
def dateToNumpyDate(s):
s = s.decode("utf-8")
ret = datetime.datetime.strptime(s, "%d.%m.%y").isoformat()
return ret
def
obert Kern:
> On Wed, Aug 31, 2016 at 4:00 PM, Florian Lindner <mailingli...@xgm.de
> <mailto:mailingli...@xgm.de>> wrote:
>>
>> Hello,
>>
>> I have mesh (more exactly: just a bunch of nodes) description with values
>> associated to the nodes in a fi
Hello,
I have mesh (more exactly: just a bunch of nodes) description with values
associated to the nodes in a file, e.g. for a
3x3 mesh:
0 0 10
0 0.3 11
0 0.6 12
0.3 0 20
0.3 0.3 21
0.3 0.6 22
0.6 0 30
0.6 0.3 31
0.6 0.6 32
What is best way to read it in and get data structures
ble.
Best,
Florian
> On May 16, 2016 9:08 AM, "Florian Lindner" <mailingli...@xgm.de> wrote:
> > Hello,
> >
> > I have an array of shape (n, 2) from which I want to extract a random
> > sample
> > of 20% of rows. The choosen samples should be r
Hello,
I have an array of shape (n, 2) from which I want to extract a random sample
of 20% of rows. The choosen samples should be removed the original array and
moved to a new array of the same shape (n, 2).
What is the most clever way to do with numpy?
Thanks,
Florian
Hello,
I try to converse a piece of C code to the new NumPy API to get rid of the
deprecation warning.
#warning Using deprecated NumPy API, disable it by #defining
NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
As a first step I replaced arrayobject.h by numpy/npy_math.h:
#include Python.h
Hello,
I have this piece of code:
comm = MPI.COMM_WORLD
temp = np.zeros(blockSize*blockSize)
PrintNB(Communicate A to, get_left_rank())
comm.Sendrecv(sendbuf=np.ascontiguousarray(lA), dest=get_left_rank(),
recvbuf=temp)
lA = np.reshape(temp, (blockSize, blockSize))
PrintNB(Finished sending)
Hello,
I have this piece of example code
import random, numpy as np
y = []
doc_all = []
# da = np.zeros(2)
for i in range(4):
docs = range(random.randint(1, 10))
y += [i]*len(docs)
doc_all += docs
# np.append(da, np.column_stack((docs, y)), axis=0)
data =
