def plot_nn_real(df):
import numpy as np
import cPickle
from nn import nn_param
from matplotlib import pyplot as plt
f=file("nnparams.sav")
update=cPickle.load(f)
sig=(df.l0+df.l1+df.l2)*df.GaussProf
sig_noise=(df.l0+df.l1+df.l2)*df.GaussProf+df.noise
sig/=np.max(sig)
sig_noise/=np.max(sig_noise)
yval=nn_param(update, sig_noise)
plt.subplot(2,2,1)
plt.plot(sig_noise)
plt.xlabel('$\\nu$ $\mu$Hz')
N=df.Frequency.size
plt.xticks(np.arange(0,N,N/4),df.Frequency[np.arange(0,N,N/4)])
plt.title('Noisy Signal')
plt.subplot(2,2,2)
plt.plot(yval)
plt.xlabel('$\\nu$ $\mu$Hz')
N=df.Frequency.size
plt.xticks(np.arange(0,N,N/4),df.Frequency[np.arange(0,N,N/4)])
plt.title('Decoder Output')
plt.subplot(2,2,4)
plt.plot(sig)
plt.xlabel('$\\nu$ $\mu$Hz')
N=df.Frequency.size
plt.xticks(np.arange(0,N,N/4),df.Frequency[np.arange(0,N,N/4)])
plt.title('Noiseless spectra')
plt.subplots_adjust(hspace=0.34)
def compute_cost_fake(df):
"""
Pass a pandas dataframe df and then generate signal.
Call the entropy error
and return the averaged cost
"""
import numpy as np
import cPickle
from nn import nn_param
from matplotlib import pyplot as plt
from theano import tensor as T
f=file("nnparams.sav")
update=cPickle.load(f)
sig=np.asarray(df.l0)
sig_noise=np.asarray(df.l0+df.noise)
sig/=np.max(sig)
sig_noise/=np.max(sig_noise)
yval=nn_param(update,sig_noise)
return T.mean(squared_error(yval,sig))
import numpy as np
from matplotlib import pyplot as plt
import sys
sys.path.append("/home/rakesh/Rak_lib")
from nn import nn_param
from plot_nn import just_plot
import pandas as pd
import cPickle
sig=np.load("/home/rakesh/sig.npy")
sig_noise=np.load("/home/rakesh/sig_noise.npy")
df=pd.read_csv('/home/rakesh/Fake_Data/MultiTrip/Spec_numax_1956.csv')
nu=np.asarray(df.Frequency)
nu_size=nu.size
f=file("/home/rakesh/Code/Code_gpuopt/Results/nnparams.sav")
nn=cPickle.load(f)
for i in range(sig.shape[0]):
yval=nn_param(nn,sig_noise[i,:])
just_plot(sig_noise[i,:],yval,sig[i,:],df)
plt.savefig("/home/rakesh/Plots/05Feb/Train/numax_%d.png"%(nu[i]))
plt.close()
