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I am trying to take a 3*3 subset from a really large 400 x 500 ndarray of numpy. But due to some reason, I am not getting the desired result. Rather it is taking the first three rows as a whole.
Here is the code that I wrote.
subset_matrix = mat[0:3][0:3]
But this is what I am getting in my output of my Jupyter Notebook
array([[91, 88, 87, ..., 66, 75, 82],
[91, 89, 88, ..., 68, 78, 84],
[91, 89, 89, ..., 72, 80, 87]], dtype=uint8)
mat[0:3][0:3] slice the axis 0 of the 2D array twice and is equivalent to mat[0:3]. What you need is mat[0:3,0:3].
I am having two arrays and a difference arrays of first two arrays
X = [1, 5, 63, 77, 103, 148, 156, 177, 183]
Y = [3, 46, 65, 87, 129, 150, 166, 181, 186]
Diff = [ 2 41 2 10 26 2 10 4 3 3]
How to plot a scatter plot for this data with x,y,diff where the same difference value show same color using matplotlib in python?
You should use 'c' parameter to color the differences.
Please see the below code:
X = [1, 5, 63, 77, 103, 148, 156, 177, 183]
Y = [3, 46, 65, 87, 129, 150, 166, 181, 186]
Diff = [ 2, 41, 2, 10, 26, 2, 10, 4, 3]
import matplotlib.pyplot as plt
plt.scatter(X,Y,c=Diff)
I have a tuple (h) as follows:
(array([[145, 34, 26, 18, 90, 89],
[ 86, 141, 216, 167, 67, 214],
[ 18, 0, 212, 49, 232, 34],
...,
[147, 99, 73, 110, 108, 9],
[222, 133, 231, 48, 227, 154],
[184, 133, 169, 201, 162, 168]], dtype=uint8), array([[178, 58, 24, 90],
[ 3, 31, 129, 243],
[ 48, 92, 19, 108],
...,
[148, 21, 25, 209],
[189, 114, 46, 218],
[ 15, 43, 92, 61]], dtype=uint8), array([[ 17, 254, 216, ..., 126, 74, 129],
[231, 168, 214, ..., 131, 50, 107],
[ 77, 185, 229, ..., 86, 167, 61],
...,
[105, 240, 95, ..., 230, 158, 27],
[211, 46, 193, ..., 48, 57, 79],
[136, 126, 235, ..., 109, 33, 185]], dtype=uint8))
I converted it into a string s = str(h):
'(array([[ 1, 60, 249, 162, 51, 3],\n [ 57, 76, 193, 244, 17, 238],\n [ 22, 72, 101, 229, 185, 124],\n ...,\n [132, 243, 123, 192, 152, 107],\n [163, 187, 131, 47, 253, 155],\n [ 21, 3, 77, 208, 229, 15]], dtype=uint8), array([[119, 149, 215, 129],\n [146, 71, 121, 79],\n [114, 148, 121, 140],\n ...,\n [175, 121, 81, 71],\n [178, 92, 1, 99],\n [ 80, 122, 189, 209]], dtype=uint8), array([[ 26, 122, 248, ..., 104, 167, 29],\n [ 41, 213, 250, ..., 82, 71, 211],\n [ 20, 122, 4, ..., 152, 99, 121],\n ...,\n [133, 77, 84, ..., 238, 243, 240],\n [208, 183, 187, ..., 182, 51, 116],\n [ 19, 135, 48, ..., 210, 163, 58]], dtype=uint8))'
Now, I want to convert s back to a tuple. I tried using ast.literal_eval(s), but I get the following error:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3.5/ast.py", line 84, in literal_eval
return _convert(node_or_string)
File "/usr/lib/python3.5/ast.py", line 55, in _convert
return tuple(map(_convert, node.elts))
File "/usr/lib/python3.5/ast.py", line 83, in _convert
raise ValueError('malformed node or string: ' + repr(node))
ValueError: malformed node or string: <_ast.Call object at 0x76a6f770>
I could not find this exact solution anywhere. It would be great if someone could help me out.
You can't use str() on numpy arrays (wrapped in tuples or otherwise) and hope to recover the data.
First of all, the ast.literal_eval() function only supports literals and literal displays, not numpy array(...) syntax.
Next, str() on a tuple produces debugging-friendly output; tuples don't implement a __str__ string conversion hook, so their repr() representation is returned instead. Numpy arrays do support str() conversion, but their output is still but a friendly-looking string that omits a lot of detail from the actual values. In your example, those ... ellipsis dots indicate that there is more data in that part of the array, but the strings do not include those values. So you are losing data if you were to try to re-create your arrays from this.
If you need to store these tuples in a file or database column, or need to transmit them over a network connection, you need to serialise the data. Proper serialisation will preserve every detail of the arrays.
For tuples with numpy arrays, you can use pickle.dumps() to produce a bytes object that can be passed back to pickles.loads() to recreate the same value.
You can also convert invidual numpy arrays to a numpy-specific binary format, and load that format again, with the numpy.save() and numpy.load() functions (which operate directly on files, but you can pass in io.BytesIO() objects).
I receive "ValueError: setting an array element with a sequence" when running. I have tried to turn everything into a numpy array to no avail.
import matplotlib
import numpy as np
from matplotlib import pyplot
X=np.array([
np.array([1,2,3,4,5,6,7]),
np.array([1,2,3,4,5,6,7]),
np.array([1,2,3,4,5,6,6.5,7.5]),
np.array([1,2,3,4,5,6,7,8]),
np.array([1,2,3,4,5,6,7,8,8.5]),
np.array([1,2,3,4,5,6,7,8]),
np.array([1,2,3,4,5,6,7])])
Y=np.array([
np.array([1,1,1,1,1,1,1]),
np.array([2,2,2,2,2,2,2]),
np.array([3,3,3,3,3,3,2.5,3]),
np.array([4,4,4,4,4,4,4,4]),
np.array([5,5,5,5,5,5,5,5,5]),
np.array([6,6,6,6,6,6,6,6]),
np.array([7,7,7,7,7,7,7])])
Z= np.array([
np.array([4190, 4290, 4200, 4095, 4181, 4965, 4995]),
np.array([4321, 4389, 4311, 4212, 4894, 4999, 5001]),
np.array([4412, 4442, 4389, 4693, 4899, 5010, 5008, 4921]),
np.array([4552, 4651, 4900, 4921, 4932, 5020, 4935, 4735]),
np.array([4791, 4941, 4925, 5000, 4890, 4925, 4882, 4764, 4850]),
np.array([4732, 4795, 4791, 4852, 4911, 4865, 4919, 4862]),
np.array([4520, 4662, 4735,4794,4836,4852,4790])])
matplotlib.pyplot.contour(X, Y, Z)
EDIT
I sort of solved this problem by removing values from my sub-arrays in order to make the lengths equal, however I would still like to know how it is possible to feed an array containing sub-arrays of different lengths into contour plot.
The answer is to make X, Y and Z inputs all 1D arrays and to use tricontour instead of contour.
X=np.array([1,2,3,4,5,6,7,
1,2,3,4,5,6,7,
1,2,3,4,5,6,6.5,7.5,
1,2,3,4,5,6,7,8,
1,2,3,4,5,6,7,8,9,
1,2,3,4,5,6,7,8,
1,2,3,4,5,6,7])
Y=np.array([1,1,1,1,1,1,1,
2,2,2,2,2,2,2,
3,3,3,3,3,3,2.5,3,
4,4,4,4,4,4,4,4,
5,5,5,5,5,5,5,5,5,
6,6,6,6,6,6,6,6,
7,7,7,7,7,7,7])
Z= np.array([80, 73, 65, 57, 61, 55, 60,
78, 73, 71, 55, 55, 60, 90,
65, 62, 61, 61, 51, 60, 71, 78,
70, 58, 58, 65, 80, 81, 90, 81,
80, 59, 51, 58, 70, 70, 90, 89, 78,
90, 63, 55, 58, 65, 78, 79, 70,
100, 68, 54,52,60,72,71])
Y=np.flip(Y,0)
asdf=matplotlib.pyplot.tricontour(X, Y, Z,11)
matplotlib.pyplot.xlim([1,8])
matplotlib.pyplot.ylim([1,7])
matplotlib.pyplot.clabel(asdf, fontsize=6, inline=0)
matplotlib.pyplot.show()
I am struggling with converting a Node application to Ruby. I have a Buffer of integers that I need to encode as an ASCII string.
In Node this is done like this:
const a = Buffer([53, 127, 241, 120, 57, 136, 112, 210, 162, 200, 111, 132, 46, 146, 210, 62, 133, 88, 80, 97, 58, 139, 234, 252, 246, 19, 191, 84, 30, 126, 248, 76])
const b = a.toString('hex')
// b = "357ff178398870d2a2c86f842e92d23e855850613a8beafcf613bf541e7ef84c"
const c = a.toString('ascii')
// c = '5qx9\bpR"Ho\u0004.\u0012R>\u0005XPa:\u000bj|v\u0013?T\u001e~xL'
I want to get the same output in Ruby but I don't know how to convert a to c. I used b to validate that a is parsed the same in Ruby and Node and it looks like it's working.
a = [53, 127, 241, 120, 57, 136, 112, 210, 162, 200, 111, 132, 46, 146, 210, 62, 133, 88, 80, 97, 58, 139, 234, 252, 246, 19, 191, 84, 30, 126, 248, 76].pack('C*')
b = a.unpack('H*')
# ["357ff178398870d2a2c86f842e92d23e855850613a8beafcf613bf541e7ef84c"]
# c = ???
I have tried serveral things, virtually all of the unpack options, and I also tried using the encode function but I lack the understanding of what the problem is here.
Okay well I am not that familiar with Node.js but you can get fairly close with some basic understandings:
Node states:
'ascii' - For 7-bit ASCII data only. This encoding is fast and will strip the high bit if set.
Update After rereading the nod.js description I think it just means it will drop 127 and only focus on the first 7 bits so this can be simplified to:
def node_js_ascii(bytes)
bytes.map {|b| b % 128 }
.reject(&127.method(:==))
.pack('C*')
.encode(Encoding::UTF_8)
end
node_js_ascii(a)
#=> #=> "5qx9\bpR\"Ho\u0004.\u0012R>\u0005XPa:\vj|v\u0013?T\u001E~xL"
Now the only differences are that node.js uses "\u000b" to represent a vertical tab and ruby uses "\v" and that ruby uses uppercase characters for unicode rather than lowercase ("\u001E" vs "\u001e") (you could handle this if you so chose)
Please note This form of encoding is not reversible due to the fact that you have characters that are greater than 8 bits in your byte array.
TL;DR (previous explanation and solution only works up to 8 bits)
Okay so we know the max supported decimal is 127 ("1111111".to_i(2)) and that node will strip the high bit if set meaning [I am assuming] 241 (an 8 bit number will become 113 if we strip the high bit)
With that understanding we can use:
a = [53, 127, 241, 120, 57, 136, 112, 210, 162, 200, 111, 132, 46, 146, 210, 62, 133, 88, 80, 97, 58, 139, 234, 252, 246, 19, 191, 84, 30, 126, 248, 76].map do |b|
b < 128 ? b : b - 128
end.pack('C*')
#=> "5\x7Fqx9\bpR\"Ho\x04.\x12R>\x05XPa:\vj|v\x13?T\x1E~xL"
Then we can encode that as UTF-8 like so:
a.encode(Encoding::UTF_8)
#=> "5\u007Fqx9\bpR\"Ho\u0004.\u0012R>\u0005XPa:\vj|v\u0013?T\u001E~xL"
but there is still is still an issue here.
It seems Node.js also ignores the Delete (127) when it converts to 'ascii' (I mean the high bit is set but if we strip it then it is 63 ("?") which doesn't match the output) so we can fix that too
a = [53, 127, 241, 120, 57, 136, 112, 210, 162, 200, 111, 132, 46, 146, 210, 62, 133, 88, 80, 97, 58, 139, 234, 252, 246, 19, 191, 84, 30, 126, 248, 76].map do |b|
b < 127 ? b : b - 128
end.pack('C*')
#=> "5\xFFqx9\bpR\"Ho\x04.\x12R>\x05XPa:\vj|v\x13?T\x1E~xL"
a.encode(Encoding::UTF_8, undef: :replace, replace: '')
#=> "5qx9\bpR\"Ho\u0004.\u0012R>\u0005XPa:\vj|v\u0013?T\u001E~xL"
Now since 127 - 128 = -1 (negative signed bit) becomes "\xFF" an undefined character in UTF-8 so we add undef: :replace what to do when the character is undefined use replace and we add replace: '' to replace with nothing.