7:00 – 5:00 ASRC NASA GOES-R

• Create spreadsheets for tasks and bugs
• More dissertation. Add Axelrod
• Mission Drive today, no meeting with Wayne, I think
• Good visualization tool finds this morning:
  • Altair: Declarative Visualization in Python
  • deck.gl is a WebGL-powered framework for visual exploratory data analysis of large datasets.
• Matrix Class
  • Change test and train within the class to input and target
  • Create the test and train as an output with the rectangular matrix with masks included. This means that I have to re-assemble that matrix from the input and target matrices
  • I still like the idea of persisting these values as excel spreadsheets
  • And now a pile of numbers that makes me happy:

    MatrixScalar:
    	rows = 5
    	input_size = 5
    	target_size = 5
    	mask_value(hex) = -1
    	tmax_cols = 6
    	mat_min = 0.13042279608514273
    	mat_max = 9.566827711787509
    
    input_npmat = 
    [4.384998306058251, 6.006494724381491, 7.061283542583833, 7.817876758859971, 7.214499436254831]
    [0.15061642402352393, 2.818956354589415, 5.04113793598655, 6.31250083574919]
    [2.8702355283795837, 5.564035171373476, 7.81403258383623, 8.590265450278785, 9.566827711787509]
    [0.1359688602006689, 0.8005043254115471, 2.080391037187722, 1.9828746089685887, 2.4669996344853677]
    [0.33676501126574077]
    
    target_npmat = 
    [6.529725859535821, 4.8702784287160075, 3.677355933557321, 1.5184287945320327, -0.5429800453619322]
    [7.629655798004273, 8.043579124885415, 7.261429015491849, 7.137935661381686, 5.583232751491164]
    [8.997538924797388, 8.32502866049641, 6.5215023090524085, 4.725363596736856, 1.3761131232325439]
    [2.270623038824647, 2.430147101210101, 2.0903103552937132, 1.6846416494136842, 1.4289540998497225]
    [1.897999998722116, 1.9054555934093833, 2.883358420829866, 3.703791108487346, 4.011103843736698]
    
    scaled_input_npmat = 
    [0.5608937619909073, 0.7683025595887466, 0.9032226729055693, 0.9999999999999999, 0.9228208193584869]
    [0.023860024409113324, 0.44656728417761093, 0.798596002940291, 1.0]
    [0.30001956916639155, 0.5815966733171017, 0.8167840813322457, 0.8979220394754807, 1.0]
    [0.055115071076624986, 0.32448497933342324, 0.8432879389630332, 0.8037595876588889, 1.0]
    [1.0]
    
    scaled_target_npmat = 
    [0.8352300836842569, 0.6229668973991612, 0.47037783364770835, 0.1942252150254483, -0.06945364606145459]
    [1.2086581842168989, 1.2742301877146247, 1.1503252362944103, 1.1307619352630993, 0.88447239798718]
    [0.9404934630223677, 0.8701974062142825, 0.6816786614665502, 0.4939321308059752, 0.143842155904721]
    [0.9203986117729256, 0.9850618002693972, 0.847308741385268, 0.6828706522143898, 0.5792275279958895]
    [5.635977418165948, 5.658116281877606, 8.561929904750741, 10.998147030080538, 11.910690569251393]
    
    scaled, masked input = 
    [[ 0.56089376  0.76830256  0.90322267  1.          0.92282082]
     [-1.          0.02386002  0.44656728  0.798596    1.        ]
     [ 0.30001957  0.58159667  0.81678408  0.89792204  1.        ]
     [ 0.05511507  0.32448498  0.84328794  0.80375959  1.        ]
     [-1.         -1.         -1.         -1.          1.        ]]
    scaled target = 
    [0.8352300836842569, 0.6229668973991612, 0.47037783364770835, 0.1942252150254483, -0.06945364606145459]
    [1.2086581842168989, 1.2742301877146247, 1.1503252362944103, 1.1307619352630993, 0.88447239798718]
    [0.9404934630223677, 0.8701974062142825, 0.6816786614665502, 0.4939321308059752, 0.143842155904721]
    [0.9203986117729256, 0.9850618002693972, 0.847308741385268, 0.6828706522143898, 0.5792275279958895]
    [5.635977418165948, 5.658116281877606, 8.561929904750741, 10.998147030080538, 11.910690569251393]
    
    scaled = [ 6.52972586  4.87027843  3.67735593  1.51842879 -0.54298005], error = 0.0
    scaled = [7.6296558  8.04357912 7.26142902 7.13793566 5.58323275], error = 0.0
    scaled = [8.99753892 8.32502866 6.52150231 4.7253636  1.37611312], error = 0.0
    scaled = [2.27062304 2.4301471  2.09031036 1.68464165 1.4289541 ], error = 0.0
    scaled = [1.898      1.90545559 2.88335842 3.70379111 4.01110384], error = 0.0
    
    input_train = 
    [[ 0.05511507  0.32448498  0.84328794  0.80375959  1.        ]
     [-1.         -1.         -1.         -1.          1.        ]
     [ 0.30001957  0.58159667  0.81678408  0.89792204  1.        ]]
    
    input_test = 
    [[ 0.56089376  0.76830256  0.90322267  1.          0.92282082]
     [-1.          0.02386002  0.44656728  0.798596    1.        ]]
    
    target_train = 
    [[ 0.92039861  0.9850618   0.84730874  0.68287065  0.57922753]
     [ 5.63597742  5.65811628  8.5619299  10.99814703 11.91069057]
     [ 0.94049346  0.87019741  0.68167866  0.49393213  0.14384216]]
    
    target_test = 
    [[ 0.83523008  0.6229669   0.47037783  0.19422522 -0.06945365]
     [ 1.20865818  1.27423019  1.15032524  1.13076194  0.8844724 ]]