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Add Average Remoteness characteristic calculator #52

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ChainsManipulator opened this issue Oct 15, 2024 · 0 comments
Open

Add Average Remoteness characteristic calculator #52

ChainsManipulator opened this issue Oct 15, 2024 · 0 comments
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@masha-mtc

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@ChainsManipulator
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ChainsManipulator commented Oct 15, 2024 •
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Add calculator function for Average Remoteness characteristic for given intervals array using formula:
$$g=\displaystyle \log_2 \Delta_g = \frac{G}{n} = \frac{1}{n} * \sum_{j=1}^{m} \sum_{i=1}^{n_j} \log_2 \Delta_{ij}$$
Where $\Delta_{ij}$ is an interval and $n$ is a number of intervals in the sequence.

Examples

X = [2, 4, 2, 2, 4]
x_intervals = intervals(X, 'Start', 'Normal')
result = average_remoteness(x_intervals)
print(result)
> 0.7169925

X = ['B','B','A','A','C','B','A','C','C','B']
x_intervals = intervals(X, 'Start', 'Lossy')
result = average_remoteness(x_intervals)
print(result)
> 1.0242

X = ['B','B','A','A','C','B','A','C','C','B']
x_intervals = intervals(X, 'Start', 'Normal')
result = average_remoteness(x_intervals)
print(result)
> 1.1077

X = ['B','B','A','A','C','B','A','C','C','B']
x_intervals = intervals(X, 'End', 'Normal')
result = average_remoteness(x_intervals)
print(result)
> 1.017

X = ['B','B','A','A','C','B','A','C','C','B']
x_intervals = intervals(X, 'Start', 'Redundant')
result = average_remoteness(x_intervals)
print(result)
> 1.0828

X = ['B','B','A','A','C','B','A','C','C','B']
x_intervals = intervals(X, 'Start', 'Cycle')
result = average_remoteness(x_intervals)
print(result)
> 1.234

X = ['A','C','T','T','G','A','T','A','C','G']  
x_intervals = intervals(X, 'Start', 'Lossy')
result = average_remoteness(x_intervals)
print(result)
> 1.6726956021

X = ['A','C','T','T','G','A','T','A','C','G'] 
x_intervals = intervals(X, 'Start', 'Normal')
result = average_remoteness(x_intervals)
print(result)
> 1.4943064208

X = ['A','C','T','T','G','A','T','A','C','G'] 
x_intervals = intervals(X, 'End', 'Normal')
result = average_remoteness(x_intervals)
print(result)
> 1.4621136113

X = ['A','C','T','T','G','A','T','A','C','G'] 
x_intervals = intervals(X, 'End', 'Redundant')
result = average_remoteness(x_intervals)
print(result)
> 1.3948590506

X = ['A','C','T','T','G','A','T','A','C','G'] 
x_intervals = intervals(X, 'End', 'Cycle')
result = average_remoteness(x_intervals)
print(result)
> 1.8112989210

X = ['C','C','A','C','G','C','T','T','A','C']
x_intervals = intervals(X, 'Start', 'Lossy')
result = average_remoteness(x_intervals)
print(result)
> 1.09749375

X = ['C','C','A','C','G','C','T','T','A','C']
x_intervals = intervals(X, 'Start', 'Normal')
result = average_remoteness(x_intervals)
print(result)
> 1.3299208

X = ['C','C','A','C','G','C','T','T','A','C']
x_intervals = intervals(X, 'End', 'Normal')
result = average_remoteness(x_intervals)
print(result)
> 1.17548874963

X = ['C','C','A','C','G','C','T','T','A','C']
x_intervals = intervals(X, 'End', 'Redundant')
result = average_remoteness(x_intervals)
print(result)
> 1.31922378713

X = ['C','C','A','C','G','C','T','T','A','C'] 
x_intervals = intervals(X, 'End', 'Cycle')
result = average_remoteness(x_intervals)
print(result)
> 1.5076815597

X = ['C','G'] 
x_intervals = intervals(X, 'Start', 'Normal')
result = average_remoteness(x_intervals)
print(result)
> 0.5

X = ['C','G'] 
x_intervals = intervals(X, 'End', 'Normal')
result = average_remoteness(x_intervals)
print(result)
> 0.5

X = ['C','G'] 
x_intervals = intervals(X, 'End', 'Redundant')
result = average_remoteness(x_intervals)
print(result)
>0.5

X = ['C','G'] 
x_intervals = intervals(X, 'End', 'Cycle')
result = average_remoteness(x_intervals)
print(result)
> 1

X = ['C','C','C','C'] 
x_intervals = intervals(X, 'Start', 'Lossy')
result = average_remoteness(x_intervals)
print(result)
> 0

X =['C','C','C','C']  
x_intervals = intervals(X, 'Start', 'Normal')
result = average_remoteness(x_intervals)
print(result)
> 0

X = ['C','C','C','C'] 
x_intervals = intervals(X, 'End', 'Normal')
result = average_remoteness(x_intervals)
print(result)
> 0

X = ['C','C','C','C'] 
x_intervals = intervals(X, 'End', 'Redundant')
result = average_remoteness(x_intervals)
print(result)
> 0

X = ['C','C','C','C'] 
x_intervals = intervals(X, 'End', 'Cycle')
result = average_remoteness(x_intervals)
print(result)
> 0

X =  ['A','C','G','T'] 
x_intervals = intervals(X, 'Start', 'Normal')
result = average_remoteness(x_intervals)
print(result)
> 1.1462406252

X = ['A','C','G','T'] 
x_intervals = intervals(X, 'End', 'Normal')
result = average_remoteness(x_intervals)
print(result)
> 1.1462406252

X = ['A','C','G','T'] 
x_intervals = intervals(X, 'End', 'Redundant')
result = average_remoteness(x_intervals)
print(result)
> 1.1462406252

X = ['A','C','G','T'] 
x_intervals = intervals(X, 'End', 'Cycle')
result = average_remoteness(x_intervals)
print(result)
> 2

X = ['A','A','A','A','C','G','T'] 
x_intervals = intervals(X, 'Start', 'Lossy')
result = average_remoteness(x_intervals)
print(result)
> 0

X = ['A','A','A','A','C','G','T'] 
x_intervals = intervals(X, 'Start', 'Normal')
result = average_remoteness(x_intervals)
print(result)
> 1.102035074

X = ['A','A','A','A','C','G','T'] 
x_intervals = intervals(X, 'End', 'Normal')
result = average_remoteness(x_intervals)
print(result)
> 0.654994643

X = ['A','A','A','A','C','G','T'] 
x_intervals = intervals(X, 'End', 'Redundant')
result = average_remoteness(x_intervals)
print(result)
> 1.1181098199

X = ['A','A','A','A','C','G','T'] 
x_intervals = intervals(X, 'End', 'Cycle')
result = average_remoteness(x_intervals)
print(result)
> 1.4888663952

No intervals

X = ['C','G'] 
x_intervals = intervals(X, 'End', 'Lossy')
result = average_remoteness(x_intervals)
print(result)
> 0

X = ['A','C','G','T'] 
x_intervals = intervals(X, 'End', 'Lossy')
result = average_remoteness(x_intervals)
print(result)
> 0

X = [2, 1] 
x_intervals = intervals(X, 'End', 'Lossy')
result = average_remoteness(x_intervals)
print(result)
> 0

Inequalities with identifying informations

X = [10, 87, 10, 87, 10, 87] 
x_intervals = intervals(X, 'Start', 'Lossy')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [10, 87, 10, 87, 10, 87] 
x_intervals = intervals(X, 'End', 'Lossy')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [10, 87, 10, 87, 10, 87] 
x_intervals = intervals(X, 'Start', 'Normal')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [10, 87, 10, 87, 10, 87] 
x_intervals = intervals(X, 'End', 'Normal')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [10, 87, 10, 87, 10, 87] 
x_intervals = intervals(X, 'Start', 'Redundant')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [10, 87, 10, 87, 10, 87] 
x_intervals = intervals(X, 'End', 'Redundant')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [10, 87, 10, 87, 10, 87] 
x_intervals = intervals(X, 'Start', 'Cycle')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [10, 87, 10, 87, 10, 87] 
x_intervals = intervals(X, 'End', 'Cycle')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [1, 1, 3, 1, 1] 
x_intervals = intervals(X, 'Start', 'Lossy')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [1, 1, 3, 1, 1] 
x_intervals = intervals(X, 'End', 'Lossy')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [1, 1, 3, 1, 1] 
x_intervals = intervals(X, 'Start', 'Normal')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [1, 1, 3, 1, 1] 
x_intervals = intervals(X, 'End', 'Normal')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [1, 1, 3, 1, 1] 
x_intervals = intervals(X, 'Start', 'Redundant')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [1, 1, 3, 1, 1] 
x_intervals = intervals(X, 'End', 'Redundant')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [1, 1, 3, 1, 1] 
x_intervals = intervals(X, 'Start', 'Cycle')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [1, 1, 3, 1, 1] 
x_intervals = intervals(X, 'End', 'Cycle')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [13, 13, 13, 13] 
x_intervals = intervals(X, 'Start', 'Lossy')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [13, 13, 13, 13] 
x_intervals = intervals(X, 'End', 'Lossy')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [13, 13, 13, 13] 
x_intervals = intervals(X, 'Start', 'Normal')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [13, 13, 13, 13] 
x_intervals = intervals(X, 'End', 'Normal')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [13, 13, 13, 13] 
x_intervals = intervals(X, 'Start', 'Redundant')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [13, 13, 13, 13] 
x_intervals = intervals(X, 'End', 'Redundant')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [13, 13, 13, 13]  
x_intervals = intervals(X, 'Start', 'Cycle')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = [13, 13, 13, 13] 
x_intervals = intervals(X, 'End', 'Cycle')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = ['A', 'A', 'B', 'B'] 
x_intervals = intervals(X, 'Start', 'Lossy')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = ['A', 'A', 'B', 'B'] 
x_intervals = intervals(X, 'End', 'Lossy')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = ['A', 'A', 'B', 'B'] 
x_intervals = intervals(X, 'Start', 'Normal')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = ['A', 'A', 'B', 'B'] 
x_intervals = intervals(X, 'End', 'Normal')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = ['A', 'A', 'B', 'B'] 
x_intervals = intervals(X, 'Start', 'Redundant')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = ['A', 'A', 'B', 'B'] 
x_intervals = intervals(X, 'End', 'Redundant')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = ['A', 'A', 'B', 'B'] 
x_intervals = intervals(X, 'Start', 'Cycle')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = ['A', 'A', 'B', 'B'] 
x_intervals = intervals(X, 'End', 'Cycle')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = ['B'] 
x_intervals = intervals(X, 'Start', 'Lossy')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = ['B'] 
x_intervals = intervals(X, 'End', 'Lossy')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = ['B'] 
x_intervals = intervals(X, 'Start', 'Normal')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = ['B'] 
x_intervals = intervals(X, 'End', 'Normal')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = ['B'] 
x_intervals = intervals(X, 'Start', 'Redundant')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = ['B'] 
x_intervals = intervals(X, 'End', 'Redundant')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = ['B'] 
x_intervals = intervals(X, 'Start', 'Cycle')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true

X = ['B'] 
x_intervals = intervals(X, 'End', 'Cycle')
g = average_remoteness(x_intervals)
H = identifying_information(x_intervals)
print(g  <= H)
> true
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