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I have some information, which varies according to the day (x-axis), I would like to adjust it in a better way. I have used scipy.optimize.curve_fit and it fits well with the following function but I would like to adjust it in some better way.
A polynomial form would not be useful or imprecise for me, since the Y-axis values are cumulative, so it is unlikely that the curve will drop.
Could someone give me a hand on what I could add to the function to make it fit better?
Here is the data I use, as well as the formula with the parameter values.
>>> X
array([ 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])
>>> y
array([ 1, 1, 1, 1, 1, 1, 2, 2, 4, 15, 17, 38, 46,
53, 61, 75, 100, 111, 133, 142, 152, 159, 170, 174, 179, 182,
188, 190, 192, 196, 198, 199, 202, 205, 207, 214, 215, 216, 218,
224, 229, 231, 233, 236, 236, 236, 237, 237, 237, 237, 237, 237,
237, 238, 238, 238, 238, 238, 238, 238, 238, 238, 238, 238, 239,
243])
>>> def func(x,a,b):
return a*np.exp(b/x)
>>> popt,pcov=curve_fit(func,X,y)
>>> a=popt[0]
>>> b=popt[1]
>>> yvals=func(X,a,b)
>>> plot1=plt.plot(X,y,'*',label='original values')
>>> plot2=plt.plot(X,yvals,'r',label='curve_fit values')
>>> plt.show()
>>> popt
array([357.97373884, -20.60549425])
Curve Fit Plot
Thanks!
[0,
100,
7,
27,
34,
40,
41,
48,
58,
65,
75,
78,
79,
96,
126,
127,
0,
0,
2,
45,
54,
56,
57,
59,
66,
67,
82,
86,
102,
124,
133,
0,
0,
35,
39,
52,
53,
60,
61,
80,
81,
83,
87,
97,
98,
101,
109,
0,
0,
15,
28,
29,
30,
31,
32,
33,
37,
38,
49,
50,
51,
71,
95,
0,
0,
3,
16,
22,
23,
44,
72,
73,
74,
90,
110,
131,
0,
0,
10,
11,
18,
19,
36,
55,
89,
93,
94,
108,
113,
114,
0,
0,
1,
5,
6,
9,
12,
17,
24,
43,
64,
77,
85,
88,
91,
92,
111,
112,
130,
0,
0,
13,
20,
42,
62,
68,
84,
99,
104,
116,
119,
125,
128,
129,
132,
0,
0,
8,
14,
26,
63,
69,
70,
103,
105,
123,
0,
0,
4,
21,
25,
46,
47,
106,
107,
115,
117,
118,
120,
121,
122,
0,
0,
76,
0]
I have this list of values and I want to split is between every two zeroes.
So my list will look like this:
[0, 100, 7, 27, 34, 40, 41, 48, 58, 65, 75, 78, 79, 96, 126, 127, 0],[ 0, 2, 45, 54, 56, 57, 59, 66, 67, 82, 86, 102, 124, 133, 0],[ 0, 35, 39, 52, 53, 60, 61, 80, 81, 83, 87, 97, 98, 101, 109, 0],[ 0, 15, 28, 29, 30, 31, 32, 33, 37, 38, 49, 50, 51, 71, 95, 0],[ 0, 3, 16, 22, 23, 44, 72, 73, 74, 90, 110, 131, 0][ 0, 10, 11, 18, 19, 36, 55, 89, 93, 94, 108, 113, 114, 0],[ 0, 1, 5, 6, 9, 12, 17, 24, 43, 64, 77, 85, 88, 91, 92, 111, 112, 130, 0],[ 0, 13, 20, 42, 62, 68, 84, 99, 104, 116, 119, 125, 128, 129, 132, 0],[ 0, 8, 14, 26, 63, 69, 70, 103, 105, 123, 0],[ 0, 4, 21, 25, 46, 47, 106, 107, 115, 117, 118, 120, 121, 122, 0][ 0, 76, 0]
Can someone help me out?
you may use a simple for loop with the built-in function zip:
# l is your list
result = [[l[0]]]
for i, j in zip(l[1:], l):
if i == 0 == j:
result.append([i])
else:
result[-1].append(i)
result
output:
[[0, 100, 7, 27, 34, 40, 41, 48, 58, 65, 75, 78, 79, 96, 126, 127, 0],
[0, 2, 45, 54, 56, 57, 59, 66, 67, 82, 86, 102, 124, 133, 0],
[0, 35, 39, 52, 53, 60, 61, 80, 81, 83, 87, 97, 98, 101, 109, 0],
[0, 15, 28, 29, 30, 31, 32, 33, 37, 38, 49, 50, 51, 71, 95, 0],
[0, 3, 16, 22, 23, 44, 72, 73, 74, 90, 110, 131, 0],
[0, 10, 11, 18, 19, 36, 55, 89, 93, 94, 108, 113, 114, 0],
[0, 1, 5, 6, 9, 12, 17, 24, 43, 64, 77, 85, 88, 91, 92, 111, 112, 130, 0],
[0, 13, 20, 42, 62, 68, 84, 99, 104, 116, 119, 125, 128, 129, 132, 0],
[0, 8, 14, 26, 63, 69, 70, 103, 105, 123, 0],
[0, 4, 21, 25, 46, 47, 106, 107, 115, 117, 118, 120, 121, 122, 0],
[0, 76, 0]]
I think the easiest way is to do this:
outputList=[]
start = 0
for i in range(len(myList) - 1):
curr = myList[i]
next = myList[i+1]
if curr == 0 and next == 0:
outputList.append(myList[start:i+1]
start = i+1
I think that will do the job, let me now if that worked! :D
Why my boxplot is not showing the expected output? I can see only circles and instead I'll want to see a traditional boxplot. How can I fix it?
import matplotlib as plt
collection_0 = [826, 58, 305, 161, 341, 25, 50, 1303, 1241, 406, 4318, 14330, 62, 45, 17, 809, 2560, 2901, 1988, 1755, 2584, 1924, 218, 13, 140, 156, 591, 109, 17, 563, 242, 23, 156, 179, 85, 59, 78, 55, 57, 27, 33, 62, 499, 685, 1418, 70, 155, 388, 205, 62, 22, 358, 688, 273, 27, 107, 85, 856, 375, 144, 476, 161, 33, 1748, 315, 106, 347, 85, 43, 157, 770, 616, 220, 13, 170, 156, 200, 165, 1211, 138, 163, 61, 78, 140, 318, 1296, 14, 386, 19, 918, 193, 381, 178, 106, 91, 109, 261, 72, 436, 194, 176, 237, 28, 201, 36, 166, 1928, 358, 611, 58, 82, 59, 37, 269, 223, 836, 45, 425, 166, 26, 63, 387, 270, 180, 331, 342, 629, 610, 46, 67, 151, 57, 188, 70, 96, 41, 92, 79, 26, 56, 188, 466, 214, 45, 39, 161, 70, 134, 370, 70, 401, 85, 113, 224, 60, 508, 58, 71, 49, 56, 400, 1308, 22, 124, 74, 63, 56, 84, 144, 26, 29, 33, 20, 241, 25, 17, 25, 45, 37, 100, 93, 175, 27, 308, 134, 28, 203, 195, 161, 168, 364, 102, 66, 53, 57, 195, 30, 55, 108, 110, 75, 42, 531, 25, 17, 156, 24, 29, 303, 77, 36, 184, 67, 15, 92, 124, 206, 51, 87, 83, 23, 134, 64, 50, 99, 451, 144, 265, 228, 96, 357, 39, 14, 91, 46, 110, 75, 18, 30, 93, 61, 31, 203, 226, 92, 162, 415, 30, 48, 86, 51, 79, 130, 181, 17, 64, 57, 168, 153, 72, 57, 34, 234, 18, 30, 72, 98, 44, 114, 58, 23, 54, 24, 126, 37, 28, 73, 8, 38, 86, 214, 46, 34, 63, 79, 72, 111, 37, 499, 382, 76, 589, 72, 139, 108, 301, 63, 158, 17, 12, 103, 337, 65, 17, 56, 32, 27, 14, 224, 33, 40, 55, 60, 76, 18, 24, 56, 99, 135, 23, 50, 102, 74, 114, 29, 24, 50, 84, 33, 316, 52, 38, 112, 61, 10, 22, 17, 71, 22, 99, 51, 84, 34, 32, 18, 91, 240, 29, 141, 121, 67, 40, 303, 78, 86, 48, 149, 102, 57, 42, 88, 137, 133, 89, 88, 70, 31, 24, 73, 7, 53, 46, 156, 17, 133, 85, 103, 70, 26, 145, 26, 112, 81, 37, 27, 98, 14, 84, 26, 31, 43, 42, 19, 38, 32, 35, 92, 168, 53, 175, 25, 30, 48, 84, 98, 57, 62, 32, 38, 75, 11, 33, 29, 38, 48, 52, 244, 303, 135, 10, 52, 12, 43, 78, 34, 50, 51, 49, 68, 68, 53, 18, 50, 64, 17, 27, 17, 21, 12, 46, 29, 35, 31, 93, 93, 25, 20, 18, 18, 43, 61, 29, 16, 40, 28, 26, 15, 30, 41, 67, 75, 53, 64, 105, 15, 35, 41, 22, 54, 20, 38, 31, 21, 105, 23, 37, 12, 29, 38, 16, 16, 21, 57, 66, 83, 44, 43, 14, 28, 48, 51, 17, 21, 16, 7, 34, 50, 23, 14, 18, 23, 32, 91, 29, 31, 23, 9, 14, 17, 15, 43, 16, 17, 20, 11, 16, 7, 13, 11, 49, 42, 13, 23, 18, 28, 38, 23, 10, 32, 9, 34, 16, 18, 9, 23, 16, 12, 65, 31, 37, 16, 9, 34, 8, 12, 22, 55, 17, 30, 13, 25, 27, 14, 7, 78, 19, 11, 41, 54, 22, 27, 8, 18, 22, 6, 29, 16, 35, 27, 8, 10, 7, 51, 9, 23, 12, 9, 6, 15, 16, 8, 7, 14, 12, 10, 14, 17, 10, 13, 18, 8, 7, 9, 10, 10]
collection_1 = [1353, 25, 2430, 1995, 1209, 1291, 564, 68, 1184, 81, 132, 140, 1463, 258, 143, 338, 63, 38, 144, 534, 130, 2742, 392, 157, 301, 193, 620, 2303, 2269, 84, 1464, 148, 593, 191, 102, 1194, 211, 11, 2498, 359, 808, 552, 96, 334, 238, 46, 1771, 536, 160, 195, 318, 193, 684, 280, 249, 19, 235, 15, 144, 2030, 104, 619, 523, 106, 902, 31, 13, 55, 9, 21, 68, 51, 45, 92, 41, 432, 436, 137, 81, 57, 210, 254, 34, 28, 301, 72, 134, 409, 30, 53, 112, 106, 267, 33, 57, 35, 18, 143, 52, 45, 36, 183, 43, 66, 40, 100, 194, 139, 18, 280, 262, 62, 331, 196, 604, 56, 43, 181, 82, 171, 57, 22, 34, 52, 46, 260, 125, 50, 46, 23, 69, 83, 28, 219, 94, 32, 82, 31, 200, 20, 78, 725, 225, 107, 58, 59, 31, 44, 18, 136, 180, 74, 20, 44, 28, 90, 69, 48, 47, 50, 74, 18, 50, 20, 75, 127, 19, 80, 23, 163, 30, 103, 27, 10, 37, 37, 44, 41, 46, 49, 48, 55, 14, 19, 42, 79, 50, 45, 36, 15, 45, 128, 122, 46, 38, 21, 21, 81, 24, 12, 15, 53, 9, 26, 43, 23, 16, 79, 9, 45, 146, 58, 17, 30, 13, 8, 17, 24, 56, 6, 12, 17, 9, 15, 11, 13, 13, 12, 14, 21, 10, 8, 15, 8, 28, 8, 11, 24, 9, 13, 30, 14, 15, 7, 9, 25, 7, 8, 10, 5, 7, 7, 6, 7, 6, 7, 8, 9]
data_to_plot = [collection_0, collection_1]
box = plt.boxplot(data_to_plot,patch_artist=True, labels=["Contracting", "Expanding"])
colors = ['red', 'green']
for patch, color in zip(box['boxes'], colors):
patch.set_facecolor(color)
plt.ylabel("Unique adopters")
plt.show()
Your data varies over 4 orders of magnitude with a majority of data lying close to less than 1000. The mean of your data is around 170 and so the whole box plot appears compressed due to the huge outlier value of above 14000. You can see this via a histogram
plt.hist(collection_0);
You should try using a log scale for your expected visualization
plt.yscale('log')
I have a nested numpy array (dtype=object), it contains 333 arrays that increase consistently from size 52x1 to size 52x333
I would like to effectively extract and concatenate these arrays so that I have a single 52x55611 array
I imagine this may be straightforward but my attempts using numpy.reshape have been unsuccesful
If you want to stack them along the second axis, you can use numpy.hstack.
list_of_arrays = [ array_1, ..., array_n] #all these arrays have same shape[0]
big_array = np.hstack( list_of_arrays)
if I have understood you correctly, you could use numpy.concatenate.
>>> import numpy as np
>>> a = np.array([range(52)])
>>> b = np.array([range(52,104), range(104, 156)])
>>> np.concatenate((a,b))
array([[ 0, 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, 101, 102, 103],
[104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,
117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,
130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,
143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155]])
>>>
I want to multiply only every second number on a list(0-100) but I just can't get it to work.
[x*10 for x in range(100) if x%2==0] # take vale to be multiply instead of 10, use x if you want to multiply with number it self
below code will only multiply even elements and keep other as it is.
def iterate_lis(get_list):
ls = []
for x in get_list:
if x%2==0:
ls.append(x*2)
else:
ls.append(x)
print(ls)
return ls
iterate_count = 5 # list will be iterate 5 times
for i in range(iterate_count):
if i ==0:
get_lis = iterate_lis(range(100))
else:
get_lis = iterate_lis(get_lis)
result for iterate_count=5 will be as follow:
>>>
[0, 1, 4, 3, 8, 5, 12, 7, 16, 9, 20, 11, 24, 13, 28, 15, 32, 17, 36, 19, 40, 21, 44, 23, 48, 25, 52, 27, 56, 29, 60, 31, 64, 33, 68, 35, 72, 37, 76, 39, 80, 41, 84, 43, 88, 45, 92, 47, 96, 49, 100, 51, 104, 53, 108, 55, 112, 57, 116, 59, 120, 61, 124, 63, 128, 65, 132, 67, 136, 69, 140, 71, 144, 73, 148, 75, 152, 77, 156, 79, 160, 81, 164, 83, 168, 85, 172, 87, 176, 89, 180, 91, 184, 93, 188, 95, 192, 97, 196, 99]
[0, 1, 8, 3, 16, 5, 24, 7, 32, 9, 40, 11, 48, 13, 56, 15, 64, 17, 72, 19, 80, 21, 88, 23, 96, 25, 104, 27, 112, 29, 120, 31, 128, 33, 136, 35, 144, 37, 152, 39, 160, 41, 168, 43, 176, 45, 184, 47, 192, 49, 200, 51, 208, 53, 216, 55, 224, 57, 232, 59, 240, 61, 248, 63, 256, 65, 264, 67, 272, 69, 280, 71, 288, 73, 296, 75, 304, 77, 312, 79, 320, 81, 328, 83, 336, 85, 344, 87, 352, 89, 360, 91, 368, 93, 376, 95, 384, 97, 392, 99]
[0, 1, 16, 3, 32, 5, 48, 7, 64, 9, 80, 11, 96, 13, 112, 15, 128, 17, 144, 19, 160, 21, 176, 23, 192, 25, 208, 27, 224, 29, 240, 31, 256, 33, 272, 35, 288, 37, 304, 39, 320, 41, 336, 43, 352, 45, 368, 47, 384, 49, 400, 51, 416, 53, 432, 55, 448, 57, 464, 59, 480, 61, 496, 63, 512, 65, 528, 67, 544, 69, 560, 71, 576, 73, 592, 75, 608, 77, 624, 79, 640, 81, 656, 83, 672, 85, 688, 87, 704, 89, 720, 91, 736, 93, 752, 95, 768, 97, 784, 99]
[0, 1, 32, 3, 64, 5, 96, 7, 128, 9, 160, 11, 192, 13, 224, 15, 256, 17, 288, 19, 320, 21, 352, 23, 384, 25, 416, 27, 448, 29, 480, 31, 512, 33, 544, 35, 576, 37, 608, 39, 640, 41, 672, 43, 704, 45, 736, 47, 768, 49, 800, 51, 832, 53, 864, 55, 896, 57, 928, 59, 960, 61, 992, 63, 1024, 65, 1056, 67, 1088, 69, 1120, 71, 1152, 73, 1184, 75, 1216, 77, 1248, 79, 1280, 81, 1312, 83, 1344, 85, 1376, 87, 1408, 89, 1440, 91, 1472, 93, 1504, 95, 1536, 97, 1568, 99]
[0, 1, 64, 3, 128, 5, 192, 7, 256, 9, 320, 11, 384, 13, 448, 15, 512, 17, 576, 19, 640, 21, 704, 23, 768, 25, 832, 27, 896, 29, 960, 31, 1024, 33, 1088, 35, 1152, 37, 1216, 39, 1280, 41, 1344, 43, 1408, 45, 1472, 47, 1536, 49, 1600, 51, 1664, 53, 1728, 55, 1792, 57, 1856, 59, 1920, 61, 1984, 63, 2048, 65, 2112, 67, 2176, 69, 2240, 71, 2304, 73, 2368, 75, 2432, 77, 2496, 79, 2560, 81, 2624, 83, 2688, 85, 2752, 87, 2816, 89, 2880, 91, 2944, 93, 3008, 95, 3072, 97, 3136, 99]
You can`t multiply with 0 , because the result will be always 0.
result=1
for i in range (1 , 10 ):
if i%2==0:
result*=i
print(result)
import numpy as np
l = range(100)
np.product(l[0::2])
This will give you every second element of your list and multiply all.
I want to change to list so: 1,2,3,4,5,6,7,8 becomes 1,4,3,8,5,16 etc.
Though I don't understand how you multiply 6 to end up with 16, I assume this is what you need:
new_list = []
for x in range(1,100):
if x % 2 == 0: new_list.append(x*2)
else: new_list.append(x)
print(new_list)
If the number is divisible by 2, you multiply it with 2 and append it to a new list. If not, you just append it without multiplying.
Running this program, you get the following output:
[1, 4, 3, 8, 5, 12, 7, 16, 9, 20, 11, 24, 13, 28, 15, 32, 17, 36, 19, 40, 21, 44, 23, 48, 25, 52, 27, 56, 29, 60, 31, 64, 33, 68, 35, 72, 37, 76, 39, 80, 41, 84, 43, 88, 45, 92, 47, 96, 49, 100, 51, 104, 53, 108, 55, 112, 57, 116, 59, 120, 61, 124, 63, 128, 65, 132, 67, 136, 69, 140, 71, 144, 73, 148, 75, 152, 77, 156, 79, 160, 81, 164, 83, 168, 85, 172, 87, 176, 89, 180, 91, 184, 93, 188, 95, 192, 97, 196, 99]
Use range() to generate the indexes of the entries you want to change...
numbers = [1, 2, 3, 4, 5, 6, 7, 8]
for i in range(1, len(numbers), 2):
numbers[i] *= 2
will result in numbers containing the list
[1, 4, 3, 8, 5, 12, 7, 16]