In [1]:
import pandas as pd
from pprint import pprint
import random
directory = "data/"
sources = [["EliassonP-CommonHex-SST-1 - " , "Edge SST"],
["ScalabrinL-CommonHybrid-SST-1 - " , "CFD++ SST"],
["Chen-CommonHybrid-SA-1 - " , "MFlow SA"],
["PowellN-CommonHybrid-SA-1 - " , "FUN3D SA"]]
sections = ["S01"]
# sections = ["S01", "S02", "S03", "S04", "S05", "S06", "S07", "S08", "S09", "S10", "S11", "S12", "S13", "S14", "S15", "S16"]
levels = ["L1"]
# levels = ["L1", "L2", "L3", "L4", "L5", "L6"]
dict = {}
for level in levels:
dict[level] = {}
for source, short_name in sources:
dict[level][short_name] = {}
for section in sections:
filename = directory + source + section + '_' + level + '.csv'
import os.path
if os.path.exists(filename):
csv = pd.read_csv(filename)
dict[level][short_name][section] = csv.to_dict(outtype='dict')
print dict
dpws_dict = open("DPWS_Comparative_Data_2.py","w")
pprint(dict, dpws_dict)
dpws_dict.close()
{'L1': {'Edge SST': {'S01': {'Z': {0: 140.76199340000002, 1: 140.63299559999999, 2: 140.50199890000002, 3: 140.371994, 4: 140.24299619999999, 5: 140.23800659999998, 6: 140.23300169999999, 7: 140.2279968, 8: 140.2220001, 9: 140.21499630000002, 10: 140.20799260000001, 11: 140.1999969, 12: 140.19200130000002, 13: 140.18299869999998, 14: 140.1719971, 15: 140.16099550000001, 16: 140.14799500000001, 17: 140.1309967, 18: 140.1089935, 19: 140.08000180000002, 20: 140.03999330000002, 21: 139.98300169999999, 22: 139.9029999, 23: 139.7910004, 24: 139.6349945, 25: 139.41900630000001, 26: 139.128006, 27: 138.74099730000003, 28: 138.23899840000001, 29: 137.6000061, 30: 136.80599980000002, 31: 135.84500119999998, 32: 134.71499630000002, 33: 133.42900090000001, 34: 132.00799559999999, 35: 130.48500060000001, 36: 128.8939972, 37: 127.2669983, 38: 125.63500209999999, 39: 124.0510025, 40: 122.5889969, 41: 121.3580017, 42: 120.5, 43: 120.17099760000001, 44: 120.50700379999999, 45: 121.57800290000002, 46: 123.3700027, 47: 125.8170013, 48: 128.78399659999999, 49: 132.09199520000001, 50: 135.54800419999998, 51: 139.003006, 52: 142.3690033, 53: 145.61599730000003, 54: 148.74299619999999, 55: 151.75500490000002, 56: 154.63900759999999, 57: 157.37300109999998, 58: 159.95100400000001, 59: 162.36799619999999, 60: 164.63299559999999, 61: 166.75900269999997, 62: 168.77000430000001, 63: 170.6900024, 64: 172.53599550000001, 65: 174.28500369999998, 66: 175.8589935, 67: 177.41099550000001, 68: 179.0720062, 69: 180.73899840000001, 70: 182.36300659999998, 71: 183.9279938, 72: 185.42900090000001, 73: 186.86199950000002, 74: 188.2120056, 75: 189.46000669999998, 76: 190.58099369999999, 77: 191.5410004, 78: 192.29800419999998, 79: 192.80099490000001, 80: 193.00100710000001, 81: 192.85400390000001, 82: 192.33599850000002, 83: 191.4400024, 84: 190.18299869999998, 85: 188.6029968, 86: 186.76100159999999, 87: 184.72000119999998, 88: 182.53199770000001, 89: 180.22999569999999, 90: 177.84300229999999, 91: 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In [1]:
Content source: zenotech/zPost
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