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I have a 2D array that looks like this:
[[0.1, 0.2, 0.4, 0.6, 0.9]
[0.3, 0.7, 0.8, 0.3, 0.9]
[0.7, 0.9, 0.4, 0.6, 0.9]
[0.1, 0.2, 0.6, 0.6, 0.9]]
And I want to save the indices where the array is higher than 0.6 but I also want to keep the value of that position, so the output would be:
[0, 3, 0.6]
[0, 4, 0.9]
[1, 2, 0.7]
and so on.
To get the indices I did this:
x = np.where(PPCF> 0.6)
high_pc = np.asarray(x).T.tolist()
but how do I keep the value in a third position?
Simple, no loops:
x = np.where(PPCF > 0.6) # condition to screen values
vals = PPCF[x] # find values by indices
np.concatenate((np.array(x).T, vals.reshape(vals.size, 1)), axis = 1) # resulting array
Feel free to convert it to a list.
This should work :
x = np.where(PPCF> 0.6)
high_pc = np.asarray(x).T.tolist()
for i in high_pc:
i.append(float(PPCF[i[0],i[1]]))
You could just run a loop along the columns and rows and check if each element is greater than the threshold and save them in a list.
a = [[0.1, 0.2, 0.4, 0.6, 0.9],
[0.3, 0.7, 0.8, 0.3, 0.9],
[0.7, 0.9, 0.4, 0.6, 0.9],
[0.1, 0.2, 0.6, 0.6, 0.9]]
def find_ix(a, threshold = 0.6):
res_list = []
for i in range(len(a)):
for j in range(len(a[i])):
if a[i][j] >= threshold:
res_list.append([i, j, a[i][j]])
return res_list
print("Resulting list = \n ", find_ix(a))
import numpy as np
arr = np.array([[0.1, 0.2, 0.4, 0.6, 0.9],
[0.3, 0.7, 0.8, 0.3, 0.9],
[0.7, 0.9, 0.4, 0.6, 0.9],
[0.1, 0.2, 0.6, 0.6, 0.9]])
rows, cols = np.where(arr > 0.6) # Get rows and columns where arr > 0.6
values = arr[rows, cols] # Get all values > 0.6 in arr
result = np.column_stack((rows, cols, values)) # Stack three columns to create final array
"""
Result -
[ 0. 4. 0.9]
[ 1. 1. 0.7]
[ 1. 2. 0.8]
[ 1. 4. 0.9]
[ 2. 0. 0.7]
[ 2. 1. 0.9]
[ 2. 4. 0.9]
[ 3. 4. 0.9]]
"""
You can convert result into a list.
For the sake of illustration, imaging I have the following ndarray:
x = [[0.5, 0.3, 0.1, 0.1],
[0.4, 0.1, 0.3, 0.2],
[0.4, 0.3, 0.2, 0.1],
[0.6, 0.1, 0.1, 0.2]]
I want to sum the two vectors at columns 1 and 2 (starting the count from 0) so that the new ndarray would be:
y = [[0.5, 0.4, 0.1],
[0.4, 0.4, 0.2],
[0.4, 0.5, 0.1],
[0.6, 0.2, 0.2]]
And then, I want to average the vectors at rows 1 and 2 so that the final result would be:
z = [[0.5, 0.4, 0.1 ],
[0.4, 0.45, 0.15],
[0.6, 0.2, 0.2 ]]
Is there an efficient way to do that in numpy in one command? I really need efficiency as this operation is going to be applied in a nested loop.
Thanks in advance
#hpaulj s solution is very good, be sure to read it
You can sum columns quite easily:
a_summed = np.sum(a[:,1:3], axis=1)
You can also take the mean of multiple rows:
a_mean = np.mean(a[1:3], axis=0)
All you have to do is replace and delete the remaining columns, so it becomes:
import numpy as np
a_summed = np.sum(a[:,1:3], axis=1)
a[:, 1] = a_summed
a = np.delete(a, 2, 1)
a_mean = np.mean(a[1:3], axis=0)
a[1] = a_mean
a = np.delete(a, 2, 0)
print(a)
Since you are changing the original matrix size it would be better to do it in two steps as mentioned in the previous answers but, if you want to do it in one command, you could do it as follows and it makes for a nice generalized solution:
import numpy as np
x = np.array(([0.5, 0.3, 0.1, 0.1, 1],
[0.4, 0.1, 0.3, 0.2, 1],
[0.4, 0.3, 0.2, 0.1, 1],
[0.6, 0.1, 0.1, 0.2, 1]))
def sum_columns(matrix, col_start, col_end):
return np.column_stack((matrix[:, 0:col_start],
np.sum(matrix[:, col_start:col_end + 1], axis=1),
matrix[:, col_end + 1:]))
def avgRows_summedColumns(matrix, row_start, row_end):
return np.row_stack((matrix[0:row_start, :],
np.mean(matrix[row_start:row_end + 1, :], axis=0),
matrix[row_end:-1, :]))
# call the entire operation in one command
print(avgRows_summedColumns(sum_columns(x, 1, 2), 1, 2))
This way it doesn't matter how big your matrix is.
In [68]: x = [[0.5, 0.3, 0.1, 0.1],
...: [0.4, 0.1, 0.3, 0.2],
...: [0.4, 0.3, 0.2, 0.1],
...: [0.6, 0.1, 0.1, 0.2]]
In [69]: x=np.array(x)
ufunc like np.add have a reduceat method that lets us perform the action over groups of rows or columns. With that the first reduction is easy (but takes a little playing to understand the parameters):
In [70]: np.add.reduceat(x,[0,1,3], axis=1)
Out[70]:
array([[0.5, 0.4, 0.1],
[0.4, 0.4, 0.2],
[0.4, 0.5, 0.1],
[0.6, 0.2, 0.2]])
Apparently mean is not a ufunc, so I had to settle for add to reduce the rows:
In [71]: np.add.reduceat(Out[70],[0,1,3],axis=0)
Out[71]:
array([[0.5, 0.4, 0.1],
[0.8, 0.9, 0.3],
[0.6, 0.2, 0.2]])
and then divide by the row count to get the mean. I could generalize that to use the same [0,1,3] used in the reduceat, but for now just use a column array:
In [72]: np.add.reduceat(Out[70],[0,1,3],axis=0)/np.array([1,2,1])[:,None]
Out[72]:
array([[0.5 , 0.4 , 0.1 ],
[0.4 , 0.45, 0.15],
[0.6 , 0.2 , 0.2 ]])
and the whole thing in one expression:
In [73]: np.add.reduceat(np.add.reduceat(x,[0,1,3], axis=1),[0,1,3],axis=0)/ np.array([1,2,1])[:,None]
Out[73]:
array([[0.5 , 0.4 , 0.1 ],
[0.4 , 0.45, 0.15],
[0.6 , 0.2 , 0.2 ]])
I have the following set of 15 data points:
[0.287 , 0.0691, 0.856, 0.731, 0.895, 0.76, 0.496, 0.749, 0.77, 0.684, 0.667, 0.386, 0.4, 0.334, 0.346]
And I would like the order of these data points to be changed so to minimize the error with the following set of 15 data points:
[0.1, 0.3, 0.5, 0.7, 0.9, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.3, 0.2, 0.1]
I could just try all permutations of the first set of data points and see which one gives the smallest error but that would take forever...
I'm assuming by error you mean the summed absolute difference. It is not difficult to check that this error is minimized when a and b have the same rank order. The best reordering of a can thus be obtained using argsort
>>> a = np.array([0.287 , 0.0691, 0.856 , 0.731 , 0.895 , 0.76 , 0.496 , 0.749 , 0.77 , 0.684 , 0.667 , 0.386 , 0.4 , 0.334 , 0.346 ])
>>> b = np.array([0.1, 0.3, 0.5, 0.7, 0.9, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.3, 0.2, 0.1])
>>>
>>> best_shuffle = np.empty(a.size,int)
>>> best_shuffle[b.argsort(kind="stable")] = a.argsort(kind="stable")
>>>
>>> np.abs(b-a[best_shuffle]).sum()
1.3499000000000005
I'm looking for a similar function to tf.unsorted_segment_sum, but I don't want to sum the segments, I want to get every segment as a tensor.
So for example, I have this code:
(In real, I have a tensor with shapes of (10000, 63), and the number of segments would be 2500)
to_be_sliced = tf.constant([[0.1, 0.2, 0.3, 0.4, 0.5],
[0.3, 0.2, 0.2, 0.6, 0.3],
[0.9, 0.8, 0.7, 0.6, 0.5],
[2.0, 2.0, 2.0, 2.0, 2.0]])
indices = tf.constant([0, 2, 0, 1])
num_segments = 3
tf.unsorted_segment_sum(to_be_sliced, indices, num_segments)
The output would be here
array([sum(row1+row3), row4, row2]
What I am looking for is 3 tensor with different shapes (maybe a list of tensors), first containing the first and third rows of the original (shape of (2, 5)), the second contains the 4th row (shape of (1, 5)), the third contains the second row, like this:
[array([[0.1, 0.2, 0.3, 0.4, 0.5],
[0.9, 0.8, 0.7, 0.6, 0.5]]),
array([[2.0, 2.0, 2.0, 2.0, 2.0]]),
array([[0.3, 0.2, 0.2, 0.6, 0.3]])]
Thanks in advance!
You can do that like this:
import tensorflow as tf
to_be_sliced = tf.constant([[0.1, 0.2, 0.3, 0.4, 0.5],
[0.3, 0.2, 0.2, 0.6, 0.3],
[0.9, 0.8, 0.7, 0.6, 0.5],
[2.0, 2.0, 2.0, 2.0, 2.0]])
indices = tf.constant([0, 2, 0, 1])
num_segments = 3
result = [tf.boolean_mask(to_be_sliced, tf.equal(indices, i)) for i in range(num_segments)]
with tf.Session() as sess:
print(*sess.run(result), sep='\n')
Output:
[[0.1 0.2 0.3 0.4 0.5]
[0.9 0.8 0.7 0.6 0.5]]
[[2. 2. 2. 2. 2.]]
[[0.3 0.2 0.2 0.6 0.3]]
For your case, you can do Numpy slicing in Tensorflow. So this will work:
sliced_1 = to_be_sliced[:3, :]
# [[0.4 0.5 0.5 0.7 0.8]
# [0.3 0.2 0.2 0.6 0.3]
# [0.3 0.2 0.2 0.6 0.3]]
sliced_2 = to_be_sliced[3, :]
# [0.3 0.2 0.2 0.6 0.3]
Or a more general option, you can do it in the following way:
to_be_sliced = tf.constant([[0.1, 0.2, 0.3, 0.4, 0.5],
[0.3, 0.2, 0.2, 0.6, 0.3],
[0.9, 0.8, 0.7, 0.6, 0.5],
[2.0, 2.0, 2.0, 2.0, 2.0]])
first_tensor = tf.gather_nd(to_be_sliced, [[0], [2]])
second_tensor = tf.gather_nd(to_be_sliced, [[3]])
third_tensor = tf.gather_nd(to_be_sliced, [[1]])
concat = tf.concat([first_tensor, second_tensor, third_tensor], axis=0)
Generation of a list of many lists each with different ranges
Isc_act = [0.1, 0.2, 0.3]
I_cel = []
a = []
for i in range(0,len(Isc_act)):
a = np.arange(0, Isc_act[i], 0.1*Isc_act[i])
I_cel[i].append(a)
print(I_cel)
Output is:
IndexError: list index out of range
My code is giving error. But, I want to get I_cel = [[0,0.01,..,0.1],[0,0.02,0.04,...,0.2],[0, 0.03, 0.06,...,0.3]]. Hence, the 'nested list' I_cel has three lists and each list has 10 values.
The simplest fix to your code, probably what you were intending to do:
Isc_act = [0.1, 0.2, 0.3]
I_cel = []
for i in range(0,len(Isc_act)):
a = np.arange(0, Isc_act[i], 0.1*Isc_act[i])
I_cel.append(a)
print(I_cel)
Note that the endpoint will be one step less than you wanted! For example row zero, you have to pick two of the below:
Steps of size 0.01
Start point 0.0 and end point 0.1
10 elements total
You can not have all three.
More numpythonic approach:
>>> Isc_act = [0.1, 0.2, 0.3]
>>> (np.linspace(0, 1, 11).reshape(11,1) # [Isc_act]).T
array([[0. , 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],
[0. , 0.02, 0.04, 0.06, 0.08, 0.1 , 0.12, 0.14, 0.16, 0.18, 0.2 ],
[0. , 0.03, 0.06, 0.09, 0.12, 0.15, 0.18, 0.21, 0.24, 0.27, 0.3 ]])
linspace gives better control of the end point when dealing with floats:
In [84]: [np.linspace(0,x,11) for x in [.1,.2,.3]]
Out[84]:
[array([0. , 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ]),
array([0. , 0.02, 0.04, 0.06, 0.08, 0.1 , 0.12, 0.14, 0.16, 0.18, 0.2 ]),
array([0. , 0.03, 0.06, 0.09, 0.12, 0.15, 0.18, 0.21, 0.24, 0.27, 0.3 ])]
Or we could scale just one array (arange with integers is predictable):
In [86]: np.array([.1,.2,.3])[:,None]*np.arange(0,11)
Out[86]:
array([[0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ],
[0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. ],
[0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8, 2.1, 2.4, 2.7, 3. ]])