Re: [Numpy-discussion] Any help from Numpy community?
Hi Ryan,Thank you very much indeed, I'm not sure if I well understood your
code, let say, for the example array matrix given represents H3C-CH3
connection(bonding).
the result from your code is:
Rows:[0 0 0 0 1 1 1] # is these for C indices?
Columns: [1 2 3 4 5 6 7] # is these for H indices? but it shouldn't be 6
H's?
Atomic Distances: [ 1. 1. 1. 1. 1. 1. 1.] # ofcourse this is the
number of connections or bonds.
In fact, if I write in the form of dictionary: row indices as keys and
column indices as values,
{0:1, 0:2, 0:3, 0:4, 1:5, 1:6, 1:7}, So, does it mean C[0] is connected to
H[1], C[0] is connected to H[2] , H[1],,C[1] is connected to H[7]? But
I have only 6 H's and two C's in this example (H3C-CH3)
I have tried some thing like: but still no luck ;(
import numpy as np
from collections import defaultdict
dict = defaultdict(list)
x=2d numpy array
I = x.shape[0]
J = x.shape[1]
d={}
for i in xrange(0, I, 1):
for j in xrange(0, J, 1):
if x[i,j] 0:
dict[i].append(j)
# the result is:
dict: {0: [1, 2, 3, 4], 1: [0, 5, 6, 7], 2: [0], 3: [0], 4: [0], 5: [1],
6: [1], 7: [1]})
keys: [0, 1, 2, 3, 4, 5, 6, 7]
values: [[1, 2, 3, 4], [0, 5, 6, 7], [0], [0], [0], [1], [1], [1]]
#The H indices can be found by
H_rows = np.nonzero(x.sum(axis=1)== 1)
result=H_rows : [2, 3, 4, 5, 6, 7] # six H's
I am trying to connect this indices with the dict result but I am confused!
So, now I want to produce a dictionary or what ever to produce results as:
H[2] is connected to C[?]
H[3] is connected to C[?]
H[4] is connected to C[?], .
Thanks for any help
.
On Thu, Mar 14, 2013 at 2:26 PM, Ryan rnelsonc...@gmail.com wrote:
Birda,
I think this will get you some of the way there:
import numpy as np
x = ... # Here's your 2D atomic distance array
# Create an indexing array
index = np.arange( x.size ).reshape( x.shape )
# Find the non-zero indices
items = index[ x != 0 ]
# You only need the first half because your array is symmetric
items = items[ : items.size/2]
rows = items / x.shape[0]
cols = items % x.shape[0]
print 'Rows: ', rows
print 'Columns:', cols
print 'Atomic Distances:', x[rows, cols]
Hope it helps.
Ryan
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Whoops.
That doesn't quite work. You shouldn't drop half the items array like that.
This will work better (maybe ?):
import numpy as np
x = ... # Here's your 2D atomic distance array
index = np.arange( x.size ).reshape( x.shape )
items = index[ x != 0 ]
rows = items / x.shape[0]
cols = items % x.shape[0]
# This index mask should take better advantage of the array symmetry
mask = rows cols
print 'Rows: ', rows[mask]
print 'Columns:', cols[mask]
print 'Atomic Distances:', x[rows[mask], cols[mask]]
Ryan
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Re: [Numpy-discussion] Any help from Numpy community?
Oh, thanks alot. can the atoms = np.array(['C1', 'C2', 'H3', 'H4', 'H5',
'H6', 'H7', 'H8']) able to make general? I mean, if I have a big
molecule, it seems difficult to label each time. Ofcourse I'm new to
python(even for programing) and I didn't had any knowhow about pandas, but
i will try it. any ways, it is great help, many thanks Ryan
Birda
On Thu, Mar 14, 2013 at 5:03 PM, Ryan rnelsonc...@gmail.com wrote:
Birdada Simret birdada85 at gmail.com writes:
Hi Ryan,Thank you very much indeed, I'm not sure if I well understood
your
code, let say, for the example array matrix given represents H3C-CH3
connection(bonding).
the result from your code is:
Rows:[0 0 0 0 1 1 1] # is these for C indices?
Columns: [1 2 3 4 5 6 7] # is these for H indices? but it shouldn't be
6 H's?
Atomic Distances: [ 1. 1. 1. 1. 1. 1. 1.] # ofcourse this is the
number
of connections or bonds.
In fact, if I write in the form of dictionary: row indices as keys and
column
indices as values,
{0:1, 0:2, 0:3, 0:4, 1:5, 1:6, 1:7}, So, does it mean C[0] is connected
to
H[1], C[0] is connected to H[2] , H[1],,C[1] is connected to H[7]?
But I
have only 6 H's and two C's in this example (H3C-CH3)
I have tried some thing like: but still no luck ;(
import numpy as np
from collections import defaultdict
dict = defaultdict(list)
x=2d numpy array
I = x.shape[0]
J = x.shape[1]
d={}
for i in xrange(0, I, 1):
for j in xrange(0, J, 1):
if x[i,j] 0:
dict[i].append(j)
# the result is:
dict: {0: [1, 2, 3, 4], 1: [0, 5, 6, 7], 2: [0], 3: [0], 4: [0], 5:
[1], 6:
[1], 7: [1]})
keys: [0, 1, 2, 3, 4, 5, 6, 7]
values: [[1, 2, 3, 4], [0, 5, 6, 7], [0], [0], [0], [1], [1], [1]]
#The H indices can be found by
H_rows = np.nonzero(x.sum(axis=1)== 1)
result=H_rows : [2, 3, 4, 5, 6, 7] # six H's
I am trying to connect this indices with the dict result but I am
confused!
So, now I want to produce a dictionary or what ever to produce results
as:
H[2] is connected to C[?]
H[3] is connected to C[?]
H[4] is connected to C[?], .
Thanks for any help
.
On Thu, Mar 14, 2013 at 2:26 PM, Ryan rnelsonchem at gmail.com
wrote:
Birda,
I don't know how your getting those values from my code. Here's a slightly
modified and fully self-contained version that includes your bonding
matrix:
import numpy as np
x = np.array(
[[ 0., 1.54, 0., 0., 0., 1.08, 1.08, 1.08 ],
[ 1.54, 0., 1.08, 1.08, 1.08, 0., 0., 0. ],
[ 0., 1.08, 0., 0., 0., 0., 0., 0. ],
[ 0., 1.08, 0., 0., 0., 0., 0., 0. ],
[ 0., 1.08, 0., 0., 0., 0., 0., 0. ],
[ 1.08, 0., 0., 0., 0., 0., 0., 0. ],
[ 1.08, 0., 0., 0., 0., 0., 0., 0. ],
[ 1.08, 0., 0., 0., 0., 0., 0., 0. ]]
)
atoms = np.array(['C1', 'C2', 'H3', 'H4', 'H5', 'H6', 'H7', 'H8'])
index = np.arange( x.size ).reshape( x.shape )
items = index[ x != 0 ]
rows = items / x.shape[0]
cols = items % x.shape[0]
mask = rows cols
print 'Rows: ', rows[mask]
print 'Columns:', cols[mask]
print 'Bond Atom 1: ', atoms[ rows[mask] ]
print 'Bond Atom 2: ', atoms[ cols[mask] ]
print 'Atomic Distances:', x[rows[mask], cols[mask]]
If I copy that into a file and run it, I get the following output:
Rows:[0 0 0 0 1 1 1]
Columns: [1 5 6 7 2 3 4]
Bond Atom 1: ['C1' 'C1' 'C1' 'C1' 'C2' 'C2' 'C2']
Bond Atom 2: ['C2' 'H6' 'H7' 'H8' 'H3' 'H4' 'H5']
Atomic Distances: [ 1.54 1.08 1.08 1.08 1.08 1.08 1.08]
Honestly, I did not think about your code all that much. Too many 'for'
loops
for my taste. My code has quite a bit of fancy indexing, which I could
imagine
is also quite confusing.
If you really want a dictionary type of interface that still let's you use
Numpy
magic, I would take a look at Pandas (http://pandas.pydata.org/)
Ryan
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[Numpy-discussion] Any help from Numpy community?
* Any help from Numpy community [[ 0. 1.540. 0. 0.1.08 1.08 1.08 ] [ 1.540. 1.081.08 1.080. 0. 0. ] [0. 1.08 0. 0. 0.0. 0. 0. ] [0. 1.08 0. 0. 0.0. 0.0.] [ 0.1.080. 0. 0.0. 0.0.] [ 1.08 0. 0. 0. 0.0. 0.0. ] [ 1.08 0. 0. 0. 0.0. 0.0. ] [ 1.08 0. 0. 0. 0.0. 0.0. ]] the above is the numpy array matrix. the numbers represents: C-C: 1.54 and C-H=1.08 So I want to write this form as C of index i is connected to C of index j C of index i is connected to H of index j (C(i),C(j)) # key C(i) and value C(j) (C(i),H(j)) # key C(i) and value H(j) ; the key C(i) can be repeated to fulfil as much as the values of H(j) To summarize, the out put may look like: C1 is connected to C2 C1 is connected to H1 C1 is connected to H3 C2 is connected to H2 etc Any guide is greatly appreciated, thanks birda * ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
