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README.md: better downsampling code

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Jörg Thalheim 2016-05-25 10:28:12 +00:00
parent 5bf6236fab
commit 7b93a93e5f
2 changed files with 6 additions and 5 deletions
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README.md
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@ -32,9 +32,12 @@ import pandas as pd
# assuming the time series are stored in a tab seperated file, where `time` is # assuming the time series are stored in a tab seperated file, where `time` is
# the name of the column containing the timestamp # the name of the column containing the timestamp
df = pd.read_csv(filename, sep="\t", index_col='time', parse_dates=True) df = pd.read_csv(filename, sep="\t", index_col='time', parse_dates=True)
df = df.fillna(method="bfill", limit=1e9) # use a meaningful sample size depending on how the frequency of your time series:
# drop rows with the same time stamp # Higher is more accurate, but if series gets too long, the calculation gets cpu and memory intensive.
df = df.groupby(level=0).first() # Keeping the length below 2000 values is usually a good idea.
df = df.resample("500ms").mean()
df.interpolate(method="time", limit_direction="both", inplace=True)
df.fillna(method="bfill", inplace=True)
``` ```
- kshape also expect no time series with a constant observation value or 'n/a' - kshape also expect no time series with a constant observation value or 'n/a'

2
kshape.py
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@ -7,8 +7,6 @@ from numpy.linalg import norm
from numpy.fft import fft, ifft from numpy.fft import fft, ifft
#from scipy.linalg import eigh
def zscore(a, axis=0, ddof=0): def zscore(a, axis=0, ddof=0):
a = np.asanyarray(a) a = np.asanyarray(a)
mns = a.mean(axis=axis) mns = a.mean(axis=axis)