compile libeemd in windows

Compile libeemd in windows

Part I: Install EEMD packages

1. Install Anaconda
   • Create a new environment, e.g., EEMD_ENV
   • Use terminal in EEMD_ENV

   

2. Install m2w64-gcc, make, m2w64-pkg-config, and m2w64-gsl by using the terminal:

   • conda install make

   • conda install m2w64-pkg-config

   • conda install -c msys2 m2w64-gsl

   • conda install -c msys2 m2w64-gcc

3. Clone repos:

   • git clone https://bitbucket.org/luukko/libeemd.git

   • git clone https://bitbucket.org/luukko/pyeemd.git

4. Goto libeemd:

   • cd libeemd

5. Create a new folder ‘obj’ manually or in DOS:
   

   mkdir obj

6. Edit Makefile in libeemd:

   line 58: Delet or comment “ln -sf [email protected] libeemd.so”. This line is only for creating linkage to specific version lib. It does not work in windows.

   ln -sf [email protected] libeemd.so

7. Compile libeemd:

   make

   It will be warning:

   1 [main] make 5020 find_fast_cwd: WARNING: Couldn't compute FAST_CWD pointer.  Please report this problem to the public mailing list [email protected]
    make: uname: Command not found
    cp src/eemd.h eemd.h
    make: cp: Command not found
    Makefile:60: recipe for target `eemd.h' failed
    make: *** [eemd.h] Error 127
   

   Igore the information above. That is only because the cmd “cp src/eemd.h” does not work.
   We can copy the file “src/eemd.h” manually.

   OR remove “eemd.h” in line 30:

   all: libeemd.so.$(version) libeemd.a

   cd ..

8. Install pyeemd:

   • cd pyeemd

   • python setup.py install

9. 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 “src/eemd.h”, “libeemd.a”, and “libeemd.so.1.4.1” to pyeemd install folder.
   • Rename libeemd.so.1.4.1 to libeemd.so
10. 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.title('Test in Windows')
plt.savefig('test_win.png',dpi=326)

plt.show()

plt.close()

Part III: Q&A