In [2]:
import pickle, urllib
with open("observations.dat", "rb") as file:
observations = pickle.load(file)
obs = observations["Pooled"]
pp(obs)
{'Injected current AP amplitude test': {'data': [{'mean': 67.0,
'n': 7,
'sd': 10.583005244258363,
'sem': 4.0,
'source': u'/api/1/source/25443/'},
{'mean': 75.0,
'n': 45,
'sd': 6.708203932499369,
'sem': 1.0,
'source': u'/api/1/source/25456/'},
{'mean': 63.2,
'n': 48,
'sd': 72.05331359486529,
'sem': 10.4,
'source': u'/api/1/source/47396/'}],
'mean': array(68.77600000000001) * mV,
'n': 100,
'std': array(50.427140458738684) * mV},
'Injected current AP threshold test': {'data': [{'mean': -49.5,
'n': 3,
'sd': 1.0392304845413263,
'sem': 0.6,
'source': u'/api/1/source/25447/'},
{'mean': -33.4,
'n': 5,
'sd': 5.142956348249516,
'sem': 2.3,
'source': u'/api/1/source/25451/'},
{'mean': -33.8,
'n': 48,
'sd': 60.27536810339692,
'sem': 8.7,
'source': u'/api/1/source/47396/'}],
'mean': array(-34.60535714285714) * mV,
'n': 56,
'std': array(56.77904875355424) * mV},
'Injected current AP width test': {'data': [{'mean': 1.98,
'n': 19,
'sd': 0.43588989435406744,
'sem': 0.1,
'source': u'/api/1/source/25444/'},
{'mean': 1.65,
'n': 45,
'sd': 0.40249223594996214,
'sem': 0.06,
'source': u'/api/1/source/25456/'},
{'mean': 1.2,
'n': 48,
'sd': 1.3856406460551018,
'sem': 0.2,
'source': u'/api/1/source/47396/'}],
'mean': array(1.513125) * ms,
'n': 112,
'std': array(0.9615927154771481) * ms},
'Input resistance test': {'data': [{'mean': 32.9,
'n': 43,
'sd': 76.0662868819032,
'sem': 11.6,
'source': u'/api/1/source/25442/'},
{'mean': 128.0,
'n': 19,
'sd': 43.58898943540674,
'sem': 10.0,
'source': u'/api/1/source/25444/'},
{'mean': 115.0,
'n': 22,
'sd': 75.04665215717488,
'sem': 16.0,
'source': u'/api/1/source/25445/'},
{'mean': 124.0,
'n': 10,
'sd': 31.622776601683796,
'sem': 10.0,
'source': u'/api/1/source/25447/'},
{'mean': 107.0,
'n': 3,
'sd': 6.928203230275509,
'sem': 4.0,
'source': u'/api/1/source/25449/'},
{'mean': 208.7,
'n': 5,
'sd': 80.72205398774241,
'sem': 36.1,
'source': u'/api/1/source/25451/'},
{'mean': 118.0,
'n': 8,
'sd': 46.95189027078676,
'sem': 16.6,
'source': u'/api/1/source/25453/'},
{'mean': 68.0,
'n': 30,
'sd': 21.908902300206645,
'sem': 4.0,
'source': u'/api/1/source/25454/'},
{'mean': 59.0,
'n': 29,
'sd': 30.156922919953217,
'sem': 5.6,
'source': u'/api/1/source/25455/'},
{'mean': 236.0,
'n': 45,
'sd': 221.3707297724792,
'sem': 33.0,
'source': u'/api/1/source/25456/'},
{'mean': 103.0,
'n': 13,
'sd': 28.123299948619113,
'sem': 7.8,
'source': u'/api/1/source/25457/'},
{'mean': 214.0,
'n': 57,
'sd': 98.14784765851975,
'sem': 13.0,
'source': u'/api/1/source/25458/'},
{'mean': 47.4,
'n': 7,
'sd': 53.708751614611195,
'sem': 20.3,
'source': u'/api/1/source/25459/'},
{'mean': 240.0,
'n': 20,
'sd': 491.93495504995377,
'sem': 110.0,
'source': u'/api/1/source/25477/'},
{'mean': 299.0,
'n': 35,
'sd': 136.06983501129116,
'sem': 23.0,
'source': u'/api/1/source/25478/'},
{'mean': 19.0,
'n': 5,
'sd': 7.0,
'sem': 3.1304951684997055,
'source': u'/api/1/source/25497/'},
{'mean': 128.2,
'n': 48,
'sd': 369.96605249671217,
'sem': 53.4,
'source': u'/api/1/source/47396/'},
{'mean': 94.3,
'n': 35,
'sd': 40.5,
'sem': 6.845749463300984,
'source': u'/api/1/source/48042/'}],
'mean': array(145113133.640553) * ohm,
'n': 434,
'std': array(189410293.86777508) * ohm},
'Resting potential test': {'data': [{'mean': -51.3,
'n': 3,
'sd': 4.9,
'sem': 2.829016319029167,
'source': u'/api/1/source/1483/'},
{'mean': -65.4,
'n': 43,
'sd': 56.3939713089972,
'sem': 8.6,
'source': u'/api/1/source/25442/'},
{'mean': -62.7,
'n': 14,
'sd': 6.3608175575157,
'sem': 1.7,
'source': u'/api/1/source/25444/'},
{'mean': -56.0,
'n': 22,
'sd': 5.628498911788116,
'sem': 1.2,
'source': u'/api/1/source/25445/'},
{'mean': -53.8,
'n': 25,
'sd': 6.0,
'sem': 1.2,
'source': u'/api/1/source/25446/'},
{'mean': -61.5,
'n': 10,
'sd': 2.2135943621178655,
'sem': 0.7,
'source': u'/api/1/source/25447/'},
{'mean': -63.6,
'n': 7,
'sd': 6.878953408767936,
'sem': 2.6,
'source': u'/api/1/source/25448/'},
{'mean': -64.3,
'n': 6,
'sd': 12.002499739637573,
'sem': 4.9,
'source': u'/api/1/source/25450/'},
{'mean': -51.3,
'n': 21,
'sd': 22.454620905283615,
'sem': 4.9,
'source': u'/api/1/source/25452/'},
{'mean': -57.0,
'n': 29,
'sd': 17.232527382830412,
'sem': 3.2,
'source': u'/api/1/source/25455/'},
{'mean': -63.5,
'n': 45,
'sd': 4.024922359499621,
'sem': 0.6,
'source': u'/api/1/source/25456/'},
{'mean': -49.0,
'n': 57,
'sd': 8.304817878797826,
'sem': 1.1,
'source': u'/api/1/source/25458/'},
{'mean': -55.0,
'n': 5,
'sd': 10.95673308974897,
'sem': 4.9,
'source': u'/api/1/source/25459/'},
{'mean': -61.65,
'n': 20,
'sd': 12.074767078498866,
'sem': 2.7,
'source': u'/api/1/source/25477/'},
{'mean': -53.4,
'n': 9,
'sd': 0.6000000000000001,
'sem': 0.2,
'source': u'/api/1/source/25479/'},
{'mean': -58.2,
'n': 48,
'sd': 38.1051177665153,
'sem': 5.5,
'source': u'/api/1/source/47396/'},
{'mean': -53.9,
'n': 35,
'sd': 4.0,
'sem': 0.6761234037828132,
'source': u'/api/1/source/48042/'}],
'mean': array(-57.43458646616542) * mV,
'n': 399,
'std': array(24.630188347036064) * mV},
'Time constant test': {'data': [{'mean': 19.0,
'n': 19,
'sd': 13.076696830622023,
'sem': 3.0,
'source': u'/api/1/source/25444/'},
{'mean': 22.0,
'n': 7,
'sd': 10.583005244258363,
'sem': 4.0,
'source': u'/api/1/source/25445/'},
{'mean': 42.5,
'n': 35,
'sd': 15.973415414368965,
'sem': 2.7,
'source': u'/api/1/source/25478/'},
{'mean': 14.0,
'n': 5,
'sd': 4.5,
'sem': 2.0124611797498106,
'source': u'/api/1/source/25497/'},
{'mean': 28.1,
'n': 48,
'sd': 114.31535329954589,
'sem': 16.5,
'source': u'/api/1/source/47396/'},
{'mean': 21.3,
'n': 35,
'sd': 9.4,
'sem': 1.5888899988896112,
'source': u'/api/1/source/48042/'}],
'mean': array(27.96510067114094) * ms,
'n': 149,
'std': array(66.35918051905733) * ms}}
In [27]:
with open("results.dat", "rb") as file:
results = pickle.load(file)
print("Model,Property,Value")
for model in results.keys():
for prop in results[model].keys():
propName = prop.replace(" test","").replace("Injected current ","")
value = float(results[model][prop])
if value > 999:
value /= 1e6
print(model + "," + propName + "," + str(value))
Model,Property,Value
BhallaBower1993,Resting potential,-65.8385632851
BhallaBower1993,AP threshold,-40.3504279817
BhallaBower1993,Time constant,43.997403549
BhallaBower1993,AP width,0.533333333333
BhallaBower1993,Input resistance,51.7783849351
BhallaBower1993,AP amplitude,71.3271577373
Davison2003,Resting potential,-65.6658765828
Davison2003,AP threshold,-35.1232385971
Davison2003,Time constant,78.7822331466
Davison2003,AP width,0.425
Davison2003,Input resistance,78.4706651237
Davison2003,AP amplitude,68.1004423041
Shen1999,Resting potential,-65.0000000036
Shen1999,AP threshold,-43.6364530063
Shen1999,Time constant,22.6510221866
Shen1999,Input resistance,70.7339075844
Shen1999,AP width,0.84375
Shen1999,AP amplitude,59.3333638107
Migliore2014,Resting potential,-67.2532591441
Migliore2014,AP threshold,-47.7121541781
Migliore2014,Time constant,11.3347246215
Migliore2014,Input resistance,16.506565934
Migliore2014,AP width,1.625
Migliore2014,AP amplitude,90.557685975
Chen2002,Resting potential,-65.0
Chen2002,AP threshold,-51.527420196
Chen2002,Time constant,21.8211772408
Chen2002,Input resistance,93.0893219836
Chen2002,AP width,0.725
Chen2002,AP amplitude,69.3836366729
LiCleland2013,Resting potential,-68.9550551157
LiCleland2013,AP threshold,-37.3021882326
LiCleland2013,Time constant,41.7034119939
LiCleland2013,Input resistance,230.686567796
LiCleland2013,AP width,0.775
LiCleland2013,AP amplitude,62.668521303
OConnor2012,Resting potential,-67.9878690904
OConnor2012,AP threshold,-45.5090231248
OConnor2012,Time constant,9.72041915226
OConnor2012,AP width,9.09375
OConnor2012,Input resistance,29.9996728481
OConnor2012,AP amplitude,97.8758585355
MiglioreShepherd2008,Resting potential,-65.0000000007
MiglioreShepherd2008,AP threshold,-48.9930086054
MiglioreShepherd2008,Time constant,18.939961688
MiglioreShepherd2008,Input resistance,67.2526219928
MiglioreShepherd2008,AP width,1.875
MiglioreShepherd2008,AP amplitude,89.2747849297
In [ ]:
Content source: JustasB/MitralSuite
Similar notebooks: