In [1]:

    

import pandas as pd
import numpy as np


    

In [2]:

    

df=pd.read_hdf('./data/tables_of_fgm.h5')
df.columns


    

    
Out[2]:





Index(['zeta', 'f', 'pv', 'C2H3', 'C2H6', 'CH2', 'H2CN', 'C2H4', 'H2O2', 'C2H',
       'CN', 'heatRelease', 'NCO', 'NNH', 'N2', 'AR', 'psi', 'CO', 'CH4',
       'HNCO', 'CH2OH', 'HCCO', 'CH2CO', 'CH', 'mu', 'C2H2', 'C2H5', 'H2', 'T',
       'PVs', 'O', 'O2', 'N2O', 'C', 'C3H7', 'CH2(S)', 'NH3', 'HO2', 'NO',
       'HCO', 'NO2', 'OH', 'HCNO', 'CH3CHO', 'CH3', 'NH', 'alpha', 'CH3O',
       'CO2', 'CH3OH', 'CH2CHO', 'CH2O', 'C3H8', 'HNO', 'NH2', 'HCN', 'H', 'N',
       'H2O', 'HCCOH', 'HCNN'],
      dtype='object')

In [3]:

    

# Plotly
import plotly.offline as pyoff
import plotly.plotly as py
import plotly.tools as tls


    

In [35]:

    

list(set(df['zeta']))[5]


    

    
Out[35]:





0.55

In [55]:

    

# df_s=df.sample(frac=0.02)
df_s=df.loc[df['zeta']==list(set(df['zeta']))[4]].sample(frac=0.1)
# sp='CH2OH'
sp='PVs'
fig_db = {
    'data': [
         {'x': df_s['f'],
         'y': df_s['pv'],
         'z': df_s[sp],
         'type':'scatter3d', 
        'mode': 'markers',
          'marker':{
              'size':1
          }
         }
        
    ],
    'layout': {
        'scene':{
            'xaxis':{'title':'mixture fraction'},
            'yaxis': {'title': "progress variable"},
            'zaxis': {'title': sp}
                 }
    }
}
pyoff.iplot(fig_db, filename='multiple-scatter')


    

In [5]:

    

df_test=pd.read_hdf('sim_check.h5',key='test')
df_pred=pd.read_hdf('sim_check.h5',key='pred')


    

In [6]:

    

zeta_level=list(set(df_test['zeta']))
zeta_level.sort()


    

In [9]:

    

df_t=df_test.loc[df_test['zeta']==zeta_level[1]].sample(frac=0.5)
# df_p=df_pred.loc[df_pred['zeta']==zeta_level[1]].sample(frac=0.1)
df_p=df_pred.loc[df_t.index]
sp='PVs'
error=df_p[sp]-df_t[sp]
fig_db = {
    'data': [
        
        {'name':'test',
        'x': df_t['f'],
         'y': df_t['pv'],
         'z': df_t[sp],
         'type':'scatter3d', 
        'mode': 'markers',
          'marker':{
              'size':1
          }
        },
        {'name':'predict',
            'x': df_p['f'],
         'y': df_p['pv'],
         'z': df_p[sp],
         'type':'scatter3d', 
        'mode': 'markers',
          'marker':{
              'size':1
          },
        },
        {'name':'error',
            'x': df_p['f'],
         'y': df_p['pv'],
         'z': error,
         'type':'scatter3d', 
        'mode': 'markers',
          'marker':{
              'size':1
          },
         }       
    ],
    'layout': {
        'scene':{
            'xaxis':{'title':'mixture fraction'},
            'yaxis': {'title': "progress variable"},
            'zaxis': {'title': sp}
                 }
    }
}
pyoff.iplot(fig_db, filename='multiple-scatter')


    

In [9]:

    

import matplotlib.pyplot as plt


    

In [58]:

    

plt.scatter(df_pred[sp],df_test[sp])


    

    
Out[58]:





<matplotlib.collections.PathCollection at 0x7fdbdbfab630>






    

In [59]:

    

from scipy.interpolate import griddata
df_new=df_t
x=df_new['f']
y=df_new['pv']
z=df_new[sp]
resX=200
resY=200
xi = np.linspace(min(x), max(x), resX)
yi = np.linspace(min(y), max(y), resY)
grid_x,grid_y = np.meshgrid(xi,yi)

Z = griddata(df_new[['f','pv']],df_new[sp], (grid_x, grid_y), method='linear')


    

In [60]:

    

import plotly.graph_objs as go
data=[
    go.Contour(
    z=np.nan_to_num(Z),
    x=xi,
    y=yi    
    )
]
pyoff.iplot(data)


    

In [61]:

    

df_new=df_p
x=df_new['f']
y=df_new['pv']
z=df_new[sp]
resX=200
resY=200
xi = np.linspace(min(x), max(x), resX)
yi = np.linspace(min(y), max(y), resY)
grid_x,grid_y = np.meshgrid(xi,yi)

Z = griddata(df_new[['f','pv']],df_new[sp], (grid_x, grid_y), method='linear')
import plotly.graph_objs as go
data=[
    go.Contour(
    z=np.nan_to_num(Z),
    x=xi,
    y=yi    
    )
]
pyoff.iplot(data)


    

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