Avalanche Index

The avalanche index within APS is based on a logarithmic look-up table. The index is based on the reported number of avalanches and their size. When multiple observations are a available only the highest index is presented. However, all inidcies within a day are stored in the APS database.



In [2]:

    
# -*- coding: utf-8 -*-
%matplotlib inline
import pylab as plt
import datetime
import numpy as np
plt.rcParams['figure.figsize'] = (14, 6)



In [3]:

    
# DestructiveSizeName
DestructiveSizeName = ['1 - Harmløst(sluff)',
                      '2 - Små',
                      '3 - Middels',
                      '4 - Store',
                      '5 - Svært store']

# EstimatedNumTID
EstimatedNumTID = {
    '1':'Ett (1)',
    '2':'Noen (2-5)',
    '3':'Flere (6-10)',
    '4':'Mange (10 eller mer)'
}



In [4]:

    
# Size factor
size_factor = 6.0
amount_factor = 3.0

# look-up table
LUT = np.array([[1.0, amount_factor, amount_factor**2, amount_factor**3],
               [size_factor, size_factor*amount_factor, size_factor*amount_factor**2, size_factor*amount_factor**3],
               [size_factor**2, size_factor**2 * amount_factor, size_factor**2 * amount_factor**2, size_factor**2 *amount_factor**3],
               [size_factor**3, size_factor**3 * amount_factor, size_factor**3 * amount_factor**2, size_factor**3 * amount_factor**3],
               [size_factor**4, size_factor**4 * amount_factor, size_factor**4 * amount_factor**2, size_factor**4 * amount_factor**3]])

plt.pcolormesh(np.log(LUT), cmap=plt.cm.magma)
#plt.colorbar()

print(LUT)









    



[[  1.00000000e+00   3.00000000e+00   9.00000000e+00   2.70000000e+01]
 [  6.00000000e+00   1.80000000e+01   5.40000000e+01   1.62000000e+02]
 [  3.60000000e+01   1.08000000e+02   3.24000000e+02   9.72000000e+02]
 [  2.16000000e+02   6.48000000e+02   1.94400000e+03   5.83200000e+03]
 [  1.29600000e+03   3.88800000e+03   1.16640000e+04   3.49920000e+04]]



In [5]:

    
size1 = {'1': LUT[0,0], '2': LUT[0,1], '3': LUT[0,2], '4': LUT[0,3]}
size2 = {'1': LUT[1,0], '2': LUT[1,1], '3': LUT[1,2], '4': LUT[1,3]}
size3 = {'1': LUT[2,0], '2': LUT[2,1], '3': LUT[2,2], '4': LUT[2,3]}
size4 = {'1': LUT[3,0], '2': LUT[3,1], '3': LUT[3,2], '4': LUT[3,3]}
size5 = {'1': LUT[4,0], '2': LUT[4,1], '3': LUT[4,2], '4': LUT[4,3]}

avalanche_index = {'1 - Harmløst(sluff)': size1,
                   '2 - Små': size2,
                   '3 - Middels': size3,
                   '4 - Store': size4,
                   '5 - Svært store': size5}



In [6]:

    
# Ragnars index

size1 = {'1': 3, '2': 5, '3': 7, '4': 8}
size2 = {'1': 4, '2': 9, '3': 12, '4': 13}
size3 = {'1': 6, '2': 11, '3': 16, '4': 17}
size4 = {'1': 10, '2': 15, '3': 19, '4': 20}
size5 = {'1': 14, '2': 18, '3': 21, '4': 22}

avalanche_index = {'1 - Harmløst(sluff)': size1,
                   '2 - Små': size2,
                   '3 - Middels': size3,
                   '4 - Store': size4,
                   '5 - Svært store': size5}



In [18]:

    
example_sizes = [['3 - Middels', '4 - Store', '3 - Middels'],['3 - Middels', '2 - Små'], ['1 - Harmløst(sluff)', '2 - Små'], ['3 - Middels']]
example_amount = [['2', '3', '2'], ['2', '4'], ['4', '3'], ['2']]



In [19]:

    
avalanche_index['2 - Små']['2']









    Out[19]:





9



In [20]:

    
result = []
all_val = []
days = []
i = 0
for s, n in zip(example_sizes, example_amount):
    day_score = []
    for si, ni in zip(s ,n):
        day_score.append(avalanche_index[si][ni]) # calculate individual scores
        all_val.append(avalanche_index[si][ni])
        days.append(i)
        print("{0} {1} = {2}".format(si, ni, day_score[-1]))
    result.append(np.array(day_score).max())
    i += 1
print(result)









    



3 - Middels 2 = 11
4 - Store 3 = 19
3 - Middels 2 = 11
3 - Middels 2 = 11
2 - Små 4 = 13
1 - Harmløst(sluff) 4 = 8
2 - Små 3 = 12
3 - Middels 2 = 11
[19, 13, 12, 11]



In [21]:

    
#plt.bar(left=range(len(result)), height=result)
plt.scatter(days, all_val, c=all_val, vmin=0, vmax=22, s=np.array(all_val)**2, cmap=plt.cm.magma, zorder=10, alpha=0.3)
plt.ylim([0,23])
plt.axhline(100, linestyle='--', color='grey') # a value of 100 corresponds to considerable avalanche activity









    Out[21]:





<matplotlib.lines.Line2D at 0x1c15e405f28>

TODO: Make an index for all observations of avalanche activity and a separate one regarding observerse with >=*** only.



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