In [2]:

    

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

import pandas as pd
import datetime as dt


    

In [3]:

    

import weatherfeed as wf


    

In [4]:

    

filename = './results/model02results.csv'

Ttuile = pd.read_csv( filename, index_col=0, parse_dates=True )


    

In [358]:

    

Ttuile.plot(figsize=(14, 5) ); plt.ylabel('T_tuile °C');


    

In [274]:

    

Ttuile.columns


    

    
Out[274]:





Index(['bastille', 'vercors', 'inertie_bastille'], dtype='object')

In [275]:

    

Ttuile['bastille'].mean()


    

    
Out[275]:





27.28864370320375

In [276]:

    

surface_toiture = 37 # m2
ep_isolation = 0.2 # m

k_isolation = 0.035 # conductivité, laine de verre( ? ) 0.03 W/m/K - 0.04 W/m/K


    

In [277]:

    

Sh_pan = surface_toiture*k_isolation/ep_isolation

print( Sh_pan )


    

    




6.4750000000000005

In [278]:

    

flux = Sh_bastille * 20
print( flux )


    

    




129.5

In [279]:

    

k_isolation/ep_isolation * 20


    

    
Out[279]:





3.5000000000000004

In [359]:

    

# walls def. : 

T_nodes = Ttuile.copy()
Sh_nodes = {'bastille': Sh_pan, 'vercors' : Sh_pan}


    

In [360]:

    

T_nodes = T_nodes.resample('10min').interpolate(method='quadratic')


    

In [361]:

    

T_nodes.plot(figsize=(14, 5) ); plt.ylabel('T_tuile °C');


    

In [418]:

    

M = 1750000


    

In [419]:

    

righthandside = np.zeros( len( T_nodes ) )
somme_Sh = 0

for name in walls_def.keys():
    righthandside += T_nodes[ name ] * Sh_nodes[ name ]
    somme_Sh += Sh_nodes[ name ]


    

In [420]:

    

# TF (reel)
righthandside_TF = np.fft.rfft( righthandside  )
freq = np.fft.rfftfreq( righthandside.size, d=10*60 )


    

In [421]:

    

# modele
T_theo_TF = righthandside_TF / ( 2j*np.pi*freq*M + somme_Sh  )

# TF inverse
T_theo = np.fft.irfft( T_theo_TF, n=len( T_nodes ) )


    

In [427]:

    

T_nodes['T_theo']= T_theo
T_nodes['T_int']= df['T_int']


    

In [431]:

    

T_nodes.plot( figsize=(14, 5) ); plt.ylabel('T_tuile °C'); #plt.ylim([22, 35])


    

In [424]:

    

import emoncmsfeed as getfeeds


    

In [425]:

    

dataframefreq = '10min'
feeds =  {  'T_int':3 } # 'T_ext':2,


    

In [426]:

    

df = getfeeds.builddataframe( feeds, dataframefreq ) # startdate=pd.to_datetime('22/06/2017') 
df.plot();


    

In [371]:

    

df['T_theo'] = T_nodes['T_theo']


    

In [372]:

    

df.plot( figsize=(14, 5) )


    

    
Out[372]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fe685929b00>






    

In [379]:

    

T_nodes['T_int']= df['T_int']


    

In [380]:

    

T_nodes.plot( figsize=(14, 5) )


    

    
Out[380]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fe687a69898>






    

In [ ]: