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Here's my code :
numbers = [76, 83, 16, 69, 52, 78, 10, 77, 45, 52, 32, 17, 58, 54, 79, 72, 55, 50, 81, 74, 45, 33, 38, 10, 40, 44, 70, 81, 79, 28, 83, 41, 14, 16, 27, 38, 20, 84, 24, 50, 59, 71, 1, 13, 56, 91, 29, 54, 65, 23, 60, 57, 13, 39, 58, 94, 94, 42, 46, 58, 59, 29, 69, 60, 83, 9, 83, 5, 64, 70, 55, 89, 67, 89, 70, 8, 90, 17, 48, 17, 94, 18, 98, 72, 96, 26, 13, 7, 58, 67, 38, 48, 43, 98, 65, 8, 74, 44, 92]
while numbers>=90:
print(numbers)
Here the Output :
Traceback (most recent call last): File "main.py", line 3, in while numbers>=90: TypeError: '>=' not supported between instances of 'list' and 'int'
numbers = [76, 83, 16, 69, 52, 78, 10, 77, 45, 52, 32, 17, 58, 54, 79, 72, 55, 50, 81, 74, 45, 33, 38, 10, 40, 44, 70, 81, 79, 28, 83, 41, 14, 16, 27, 38, 20, 84, 24, 50, 59, 71, 1, 13, 56, 91, 29, 54, 65, 23, 60, 57, 13, 39, 58, 94, 94, 42, 46, 58, 59, 29, 69, 60, 83, 9, 83, 5, 64, 70, 55, 89, 67, 89, 70, 8, 90, 17, 48, 17, 94, 18, 98, 72, 96, 26, 13, 7, 58, 67, 38, 48, 43, 98, 65, 8, 74, 44, 92]
for number in numbers:
if number >= 90:
print(number)
I have a .mat file and want to convert it into a CV image format such that I can use it for a CNN model.
I am trying to obtain an RGB/ other colored image and not gray.
I tried doing the following(below) but I get a grayscale image, but when I plot the actual mat file using matplotlib it is not grayscale. Also, the .mat file has a px_spacing array apart from the image array. I am not sure how this is helpful.
def mat_to_image(mat_image):
f = loadmat(mat_image,appendmat=True)
image = np.array(f.get('I')).astype(np.float32)
mean = image.mean()
std= image.std()
print(mean, std)
hi = np.max(image)
lo = np.min(image)
image = (((image - lo)/(hi-lo))*255).astype(np.uint8)
im = Image.fromarray(image,mode='RGB')
return im
images=mat_to_image(dir/filename)
cv_img = cv2.cvtColor(np.array(images), cv2.COLOR_GRAY2RGB)
Normally plotting the .mat file fetches a non-grayscale(RGB image)
imgplot= plt.imshow(loadmat(img,appendmat=True).get('I'))
plt.show()
Here is how the mat file looks after print(loadmat('filename'))
{'__header__': b'MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Mon Sep 9 11:32:54 2019',
'__version__': '1.0',
'__globals__': [],
'I': array([
[ 81, 75, 74, 75, -11, 14, 49, 37, 29, -24, -183, -349, -581, -740],
[ 51, 33, 67, 36, 1, 42, 30, 49, 47, 42, 14, -85, -465, -727],
[ 23, 31, 36, 20, 54, 70, 44, 56, 56, 79, 62, 19, -204, -595],
[ 7, 12, 36, 47, 59, 68, 74, 56, 59, 100, 74, 34, -3, -353],
[ 23, 19, 51, 87, 86, 79, 91, 76, 96, 95, 52, 51, 74, -141],
[ 18, 51, 54, 97, 93, 94, 98, 83, 119, 71, 36, 69, 50, -16],
[ -10, 5, 53, 92, 69, 87, 103, 114, 118, 77, 51, 68, 30, 0],
[ -24, 11, 74, 80, 49, 68, 106, 129, 107, 63, 57, 70, 39, -1],
[ -45, 43, 83, 69, 43, 64, 98, 108, 90, 35, 27, 55, 31, -13],
[ -9, 32, 83, 78, 66, 106, 89, 85, 58, 43, 31, 39, 28, 7],
[ 45, 35, 76, 45, 51, 84, 55, 66, 49, 41, 39, 28, 13, -7],
[ 85, 67, 61, 45, 69, 53, 23, 32, 31, -12, -34, -182, -376, -425],
[ 136, 93, 71, 54, 30, 39, 17, -21, -29, -43, -101, -514, -792, -816]
], dtype=int16),
'px_spacing': array([[0.78125]])}
Actually i need to plot all the variations occured only in the october month of 2012 so for that i am counting the 30 rows so that i can use them in xlim for plotting.
import pandas as pd
from pandas import Series,DataFrame
import numpy as np
poll_df=pd.read_csv('http://elections.huffingtonpost.com/pollster/2012-general-election-romney-vs-obama.csv')
row_in=0
xlimit=[]
poll_df=poll_df[poll_df['Start Date'].str[:7] == '2012-10']
for date in poll_df['Start Date']:
if date[0:7] == '2012-10':
xlimit.append(row_in)
row_in += 1
else:
row_in+=1
print(min(xlimit))
print(max(xlimit))
But i don't understand why xlimit is coming out empty despite performing operations on it.
With a download of that URL I can load it with np.genfromtxt:
In [232]: data = np.genfromtxt('../Downloads/2012-general-election-romney-vs-oba
...: ma.csv',dtype=None,delimiter=',',names=True,invalid_raise=False,encodi
...: ng=None)
/usr/local/bin/ipython3:1: ConversionWarning: Some errors were detected !
Line #77 (got 13 columns instead of 17)
Line #238 (got 13 columns instead of 17)
Line #460 (got 18 columns instead of 17)
Line #488 (got 18 columns instead of 17)
Line #493 (got 13 columns instead of 17)
Line #507 (got 18 columns instead of 17)
Line #515 (got 18 columns instead of 17)
Line #538 (got 18 columns instead of 17)
Line #550 (got 18 columns instead of 17)
#!/usr/bin/python3
It's not quite as forgiving as pandas when dealing with shorter/longer length lines.
In [233]: data.shape
Out[233]: (577,)
In [234]: data.dtype
Out[234]: dtype([('Pollster', '<U56'), ('Start_Date', '<U10'), ('End_Date', '<U10'), ('Entry_DateTime_ET', '<U20'), ('Number_of_Observations', '<i8'), ('Population', '<U26'), ('Mode', '<U15'), ('Obama', '<f8'), ('Romney', '<f8'), ('Undecided', '<f8'), ('Other', '<f8'), ('Pollster_URL', '<U113'), ('Source_URL', '<U189'), ('Partisan', '<U11'), ('Affiliation', '<U5'), ('Question_Text', '?'), ('Question_Iteration', '<i8')])
The start_date field looks like:
In [235]: data['Start_Date'][:10]
Out[235]:
array(['2012-11-04', '2012-11-03', '2012-11-03', '2012-11-03',
'2012-11-03', '2012-11-03', '2012-11-03', '2012-11-01',
'2012-11-02', '2012-11-02'], dtype='
I can search it with where. I'm using astype to restrict the field to 7 characters.
In [236]: np.where(data['Start_Date'].astype('U7')=='2012-10')[0]
Out[236]:
array([18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,
53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86,
87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])
I can use usecols to get around the variable line lengths - assuming the 'bad' lines just differ in the latter fields.
In [237]: data = np.genfromtxt('../Downloads/2012-general-election-romney-vs-oba
...: ma.csv',dtype=None,delimiter=',',names=True,invalid_raise=False,encodi
...: ng=None,usecols=range(10))
In [238]: data.shape
Out[238]: (586,)
In [239]: np.where(data['Start_Date'].astype('U7')=='2012-10')[0]
Out[239]:
array([ 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57,
58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,
84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96,
97, 98, 99, 100])
I can get the same list with an iterative search like yours:
In [244]: alist = []
In [245]: for i,date in enumerate(data['Start_Date']):
...: if date[:7] == '2012-10':
...: alist.append(i)
...:
In [246]: len(alist)
Out[246]: 82
In [247]: np.array(alist)
Out[247]:
array([ 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57,
58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,
84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96,
97, 98, 99, 100])
I have a set of connected components obtained from a tree Tas follows.
To get all the connected components, I'm using the following code sample:
connectedcomponents = sorted(nx.connected_components(T), key = len,reverse=True)
This is what I'm getting;
[{66, 98, 68, 37, 7, 8, 73, 75, 44, 47, 81, 51, 19, 23, 55, 56, 58, 62, 63}, {97, 3, 6, 71, 39, 11, 77, 17, 60, 95}, {99, 5, 10, 43, 15, 20, 90, 92, 93}, {96, 4, 76, 80, 84, 53, 52}, {34, 74, 46, 18, 24, 61, 30}, {36, 9, 41, 83, 88, 57}, {65, 69, 40, 78, 21, 54}, {1, 2, 12, 13}, {89, 26, 70, 31}, {0, 42, 28, 79}, {32, 85, 86}, {59, 45, 94}, {82, 50, 22}, {64, 72}, {33, 14}, {16}, {87}, {48}, {91}, {49}, {67}, {29}, {35}, {25}, {38}, {27}]
I need to get the edges in each of these components. For an example, for the first component {66, 98, 68, 37, 7, 8, 73, 75, 44, 47, 81, 51, 19, 23, 55, 56, 58, 62, 63} I need a separate list of edges as [(37,47),(47,7),(7,62),...].
I tried it as follows:
def nodes_connected(u, v):
if u in T.neighbors(v):
return True
else:
return False
for i in connectedcomponents:
print(i)
for u,v in i:
nodes_connected("u", "v")
edges.append((u,v))
But didn't work!!!
Can someone please help me with this?
my code:
def originalList = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]
def newList = orginalList.percent(0.05,0.95) //I have no idea what I'm doing here
println newList
I have an original list of numbers, they are 1 - 100 and i want to make a new list from the original list however the new list must only have data that belongs to the sub-range 5%- 95% of the original list
so the new list must be like [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18....95]. How do i do that? i know my newList code is wrong
You mean like:
originalList[ 4..94 ] // zero starting pos
Or do you need percentages?
You could do:
originalList[ (originalList.size() * 0.05 - 1)..<(originalList.size() * 0.95) ]
You could also use the metaClass:
List.metaClass.percent { double lower, double upper ->
int d = lower * delegate.size() - 1
int t = upper * delegate.size()
delegate.take( t ).drop( d )
}
originalList.percent( 0.05, 0.95 )