def decompress(self, aggregate, df_compressed):
(n,m) = (len(df_compressed),dim)
df2 = df_compressed.apply(lambda s:pd.Series(b64_decode_series(s),dtype=np.float32))
k = df2.values.shape[1]
M = aggregate[:m]
s = aggregate[m:(m+k)]
Vh = np.reshape(aggregate[(m+k):], (k,m))
reconstructed = np.dot(np.dot(df2.values,np.diag(s)),Vh) + M
return pd.DataFrame(reconstructed,index=df_compressed.index)
def decompress(self, aggregate, df_compressed):
(n,m) = (len(df_compressed),1440)
df2 = df_compressed['compressed'].apply(lambda s:pd.Series(b64_decode_series(s),dtype=np.float32))
k = df2.values.shape[1]
M = aggregate[:m]
s = aggregate[m:(m+k)]
Vh = np.reshape(aggregate[(m+k):], (k,m))
reconstructed = np.dot(np.dot(df2.values,np.diag(s)),Vh) + M
return pd.DataFrame(reconstructed,index=df_compressed.index)
def decompress_series(self, compressed):
coeffs_flattened = b64_decode_series(compressed).tolist()
coeff_lens = [len(c) for c in pywt.wavedec(np.zeros((1440,)),self.wavelet)]
# unflatten coeffs_flattened using coeff_lens
coeffs = []
for l in coeff_lens[:self.res]:
coeffs.append(coeffs_flattened[:l])
coeffs_flattened = coeffs_flattened[l:]
# Add 0's to the rest of coeffs
coeffs = fill_coeffs(coeffs, coeff_lens)
x = pywt.waverec(coeffs,self.wavelet)
return pd.Series(x,dtype=np.float32)
def decompress_group(df):
tag = context['tag_list'][int(df.name)]
aggregate = b64_decode_series(aggregates[tag])
return compressor.decompress(aggregate, df)
def decompress_series(self, compressed):
x = b64_decode_series(compressed).tolist() * dim
return pd.Series(x, dtype=np.float32)
def get_space_mse(se):
''' Deserialize 64-bit-encoded space/error. return (space,error)'''
se = b64_decode_series(se)
n = len(se)
return se[:n/2],se[n/2:]
def decompress_group(df):
tag = context['tag_list'][int(df.name)]
aggregate = b64_decode_series(aggregates[tag])
return compressor.decompress(aggregate, df)
def decompress_series(self, compressed):
x = b64_decode_series(compressed).tolist()*dim
return pd.Series(x,dtype=np.float32)
