Note:Only for MacOS
Part I: Install EEMD packages
- Install Anaconda
- Create a new environment, e.g., EEMD_ENV
- Use terminal in EEMD_ENV
- Install gcc, make, pkg-config, and gsl by using the terminal:
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conda install -y gcc pkg-config gsl
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- Install Git:
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sudo port install git
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- Clone repos:
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git clone https://[email protected]/luukko/libeemd.git
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git clone https://[email protected]/luukko/pyeemd.git
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- Compile libeemd:
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cd libeemd
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make
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cd ..
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- Install pyeemd:
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cd pyeemd
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python setup.py install
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- Copy libeemd files
- Find out which the pyeemd install folder is, e.g., “~/anaconda/envs/EEMD_ENV/lib/python2.7/site-packages/pyeemd-1.4-py2.7.egg/pyeemd”
- Copy “eemd.h”, “libeemd.a”, “libeemd.so”, and “libeemd.so.1.4.1” to pyeemd install folder.
- Finish
Part II: Examples
Taking an example of global temperature time-series
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
import pyeemd
url = 'https://data.giss.nasa.gov/gistemp/graphs/graph_data/Global_Mean_Estimates_based_on_Land_and_Ocean_Data/graph.csv';
data = pd.read_csv(url,skiprows=1)
year = data['Year']
tmp = data['No_Smoothing']
tmp = np.array(tmp)
X = sm.add_constant(year)
model = sm.OLS(tmp, X).fit()
predictions = model.predict(X)
imfs = pyeemd.eemd(tmp)
fig = plt.figure()
ax = fig.add_subplot(111)
plt.plot(year, tmp)
eemd_lin = plt.plot(year, imfs[-1,:],label='EEMD')
reg_lin = plt.plot(year, predictions,label='Linear')
plt.legend()
plt.xlim([1879,2017])
trend_eemd = (imfs[-1,-1]-imfs[-1,0])/137.0*100.0
trend_linear = (predictions[134]-predictions[0])/137.0*100.0
plt.text(0.3,0.9, np.str(np.round(trend_eemd,3))+' $^o$C/100a'
, transform=ax.transAxes
, color = eemd_lin[0].get_color())
plt.text(0.3,0.85, np.str(np.round(trend_linear,3))+' $^o$C/100a'
, transform=ax.transAxes
, color = reg_lin[0].get_color())
plt.savefig('test.png',dpi=326)
plt.close()
Part III: Q&A
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