In [6]:
import pandas as pd

In [7]:
df=pd.read_hdf('./data/premixed_raw.H5')

In [8]:
df.columns


Out[8]:
Index(['Unnamed: 0', 'PV', 'u', 'T', 'rho', 'viscosity', 'thermalConductivity',
       'cp', 'HeatRelease', 'H2', 'H', 'O', 'O2', 'OH', 'H2O', 'HO2', 'H2O2',
       'C', 'CH', 'CH2', 'CH2(S)', 'CH3', 'CH4', 'CO', 'CO2', 'HCO', 'CH2O',
       'CH2OH', 'CH3O', 'CH3OH', 'C2H', 'C2H2', 'C2H3', 'C2H4', 'C2H5', 'C2H6',
       'HCCO', 'CH2CO', 'HCCOH', 'N', 'NH', 'NH2', 'NH3', 'NNH', 'NO', 'NO2',
       'N2O', 'HNO', 'CN', 'HCN', 'H2CN', 'HCNN', 'HCNO', 'HOCN', 'HNCO',
       'NCO', 'N2', 'AR', 'C3H7', 'C3H8', 'CH2CHO', 'CH3CHO', 'PVs', 'f'],
      dtype='object')

In [9]:
# Plotly
import plotly.offline as pyoff
import plotly.tools as tls
import pandas as pd

In [32]:
df_s=df.sample(frac=0.1)
fig_db = {
    'data': [
        {
         'x': df_s['f'],
         'y': df_s['PV'],
         'z': df_s['T'],
         'type':'scatter3d', 
        'mode': 'markers',
          'marker':{
              'size':1
          }
        }
        
    ],
    'layout': {
         'scene':{
             'xaxis':{'title':'adfasdf'},
        'yaxis': {'title': "Life Expectancy"},
         'zaxis': {'title': "Life Expectancy2"}
                 }
}
pyoff.iplot(fig_db, filename='multiple-scatter')



In [1]:
import plotly.io as pio

In [21]:
import plotly.graph_objs as go

trace0 = go.Scatter(
  x = [0,1,1,0,0,1,1,2,2,3,3,2,2,3],
  y = [0,0,1,1,3,3,2,2,3,3,1,1,0,0]
)

trace1 = go.Scatter(
  x = [0,1,2,3],
  y = [1,2,4,8],
  yaxis = "y2"
)

trace2 = go.Scatter(
  x = [1,10,100,10,1],
  y = [0,1,2,3,4],
  xaxis = "x2",
  yaxis ="y3",
)

trace3 = go.Scatter(
  x = [1,100,30,80,1],
  y = [1,1.5,2,2.5,3],
  xaxis = "x2",
  yaxis = "y4"
)

data = [trace0,trace1,trace2,trace3]

layout = go.Layout(
    width = 800,
    height = 500,
    title = "fixed-ratio axes",
    xaxis = dict(
      nticks = 10,
      domain = [0, 0.45],
      title = "shared X axis"
    ),
    yaxis = dict(
      scaleanchor = "x",
      domain = [0, 0.45],
      title = "1:1"
    ),
    yaxis2 = dict(
      scaleanchor = "x",
      scaleratio = 0.2,
      domain = [0.55,1],
      title = "1:5"
    ),
    xaxis2 = dict(
      type = "log",
      domain = [0.55, 1],
      anchor = "y3",
      title = "unconstrained log X"
    ),
    yaxis3 = dict(
      domain = [0, 0.45],
      anchor = "x2",
      title = "Scale matches ->"
    ),
    yaxis4 = dict(
      scaleanchor = "y3",
      domain = [0.55, 1],
      anchor = "x2",
      title = "Scale matches <-"
    ),
    showlegend= False
)

fig = go.Figure(data=data, layout=layout)
pyoff.iplot(fig, filename = "aspect-ratio")



In [22]:
data


Out[22]:
[Scatter({
     'x': [0, 1, 1, 0, 0, 1, 1, 2, 2, 3, 3, 2, 2, 3], 'y': [0, 0, 1, 1, 3, 3, 2, 2, 3, 3, 1, 1, 0, 0]
 }), Scatter({
     'x': [0, 1, 2, 3], 'y': [1, 2, 4, 8], 'yaxis': 'y2'
 }), Scatter({
     'x': [1, 10, 100, 10, 1], 'xaxis': 'x2', 'y': [0, 1, 2, 3, 4], 'yaxis': 'y3'
 }), Scatter({
     'x': [1, 100, 30, 80, 1], 'xaxis': 'x2', 'y': [1, 1.5, 2, 2.5, 3], 'yaxis': 'y4'
 })]

In [28]:
fig_db['data']


Out[28]:
[{'x': 43875    0.068306
  25293    0.075317
  42440    0.069308
  3193     0.131204
  55524    0.059492
  86593    0.046072
  262      0.138215
  78921    0.064701
  97102    0.070309
  10404    0.079123
  79643    0.051079
  53152    0.028044
  61431    0.084131
  77496    0.041064
  83873    0.070710
  725      0.066503
  31222    0.135210
  14573    0.076719
  44528    0.067705
  31551    0.135210
  23577    0.042065
  40742    0.057890
  88983    0.071311
  86585    0.046072
  63761    0.108168
  9344     0.144224
  8840     0.083129
  625      0.066503
  13491    0.122190
  62930    0.056488
             ...   
  25501    0.056287
  80844    0.093145
  45979    0.136212
  3846     0.127198
  4466     0.132205
  45195    0.071912
  70380    0.123191
  46940    0.085333
  65331    0.080926
  46419    0.061095
  43245    0.130202
  35366    0.064901
  2578     0.082328
  85520    0.068707
  72183    0.050078
  35541    0.064901
  7582     0.063699
  25260    0.075317
  44310    0.067705
  2135     0.074916
  11975    0.140218
  31929    0.071511
  69720    0.069909
  51677    0.075517
  5429     0.066904
  2377     0.082328
  55994    0.078522
  55902    0.078522
  74055    0.076319
  22555    0.097151
  Name: f, Length: 9750, dtype: float64, 'y': 43875    6.859777e-09
  25293    1.162485e+01
  42440    3.000472e+00
  3193     8.958410e+00
  55524    1.011970e+01
  86593    8.153831e+00
  262      9.882565e+00
  78921    1.110990e+01
  97102    1.144419e+01
  10404    1.170590e+01
  79643    9.410616e+00
  53152    2.482114e+00
  61431    9.760720e+00
  77496    7.642814e+00
  83873    3.653421e-04
  725      1.124632e+01
  31222    2.567179e-04
  14573    6.831499e-21
  44528    1.131666e+01
  31551    1.145654e+01
  23577    3.207015e+00
  40742    1.041831e+01
  88983    2.833148e-21
  86585    8.119437e+00
  63761    1.157508e+01
  9344     1.244998e+00
  8840     1.170168e+01
  625      1.101158e+01
  13491    5.363169e+00
  62930    9.793351e+00
               ...     
  25501    9.611305e+00
  80844    8.437280e-16
  45979    1.144386e+01
  3846     7.749250e+00
  4466     1.144879e+01
  45195    1.150121e+01
  70380    1.036493e+01
  46940    7.767105e+00
  65331    1.172493e-08
  46419    4.399575e-20
  43245    8.484965e+00
  35366    9.717749e-06
  2578     1.173354e+01
  85520    1.124924e+01
  72183    8.736063e+00
  35541    1.094890e+01
  7582     5.324696e+00
  25260    1.162457e+01
  44310    4.135465e+00
  2135     1.160712e+01
  11975    1.884651e+00
  31929    1.149356e+01
  69720    1.142582e+01
  51677    2.035751e+00
  5429     1.088168e+01
  2377     6.684539e+00
  55994    1.169200e+01
  55902    1.130542e+01
  74055    1.151845e+01
  22555    9.013928e+00
  Name: PV, Length: 9750, dtype: float64, 'z': 43875     300.000001
  25293    1977.720131
  42440     821.892928
  3193     1326.261227
  55524    1964.452773
  86593    1824.841042
  262      1387.842445
  78921    2134.504079
  97102    2054.259731
  10404    1921.977897
  79643    2149.023289
  53152     821.415789
  61431    1610.998633
  77496    1893.533466
  83873     300.075257
  725      2113.260392
  31222     300.009371
  14573     300.000000
  44528    2095.040579
  31551    1490.496483
  23577     908.484437
  40742    2197.828143
  88983     300.000000
  86585    1789.574397
  63761    1624.679880
  9344      486.495245
  8840     1875.824286
  625      2023.275822
  13491     991.367628
  62930    2008.346159
              ...     
  25501    1887.658387
  80844     300.000000
  45979    1486.118405
  3846     1224.620943
  4466     1494.995950
  45195    2025.666329
  70380    1465.423510
  46940    1378.238062
  65331     300.000002
  46419     300.000000
  43245    1286.389776
  35366     300.002018
  2578     1878.978855
  85520    2033.103727
  72183    1847.135983
  35541    2068.519009
  7582     1223.029947
  25260    1977.609417
  44310    1013.108910
  2135     1981.051646
  11975     571.067438
  31929    2035.212179
  69720    2060.404993
  51677     641.379911
  5429     1946.099549
  2377     1264.726081
  55994    1931.474046
  55902    1840.465454
  74055    1897.667092
  22555    1450.069502
  Name: T, Length: 9750, dtype: float64, 'type': 'scatter3d', 'mode': 'markers', 'marker': {'size': 1}}]

In [ ]: