yuyyuyu

import numpy as np
from pandas import *
from pylab import *
import matplotlib.pyplot as plt
from numpy.random import randn
fig,ax1=plt.subplots()
df = read_csv('cldata.csv')
x=df['wavelength']
y500=df['500']
y600=df['600']

yuyyuyu

import numpy as np
import matplotlib.pyplot as plt
X = np.linspace(-4,4, 2560, endpoint=True)
f=0.5
print 'set Approximation order'
n=int(raw_input())
for i in range(1,n):
  f+=2*np.sin(i/2.0*np.pi)/(i*np.pi)*np.cos(i*np.pi*X)
plt.plot(X, f)
plt.ylim((-1,4))

yuyyuyu

import numpy as np
from pandas import *
from pylab import *
import matplotlib.pyplot as plt
from numpy.random import randn

x=[i for i in range(100)]
y1=[i**2 for i in range(100)]
y2=[-i**2+10000 for i in range(100)]
y3=[5000 for i in range(100)]

yuyyuyu

import numpy as np
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
import scipy as sp

def errorfunc(x,a,b):
	return a*sp.special.erf(x)+b

xdata=np.linspace(0,4,50)
y=errorfunc(xdata,1.0,1.0)

yuyyuyu

import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage as ndimage
import skimage.filter as skif
from PIL import Image
import numpy as np
from matplotlib import pylab as plt

#open image and convert fromRGB to mono_color
img =Image.open('sudoku.jpg')

yuyyuyu

import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage as ndimage
import skimage.filter as skif


# Creating image with non-uniform background
func = lambda x, y: x * 2 + y ** 2
grid_x, grid_y = np.mgrid[-1:1:100j, -2:2:100j]
bkg = func(grid_x, grid_y)

yuyyuyu

#!/usr/bin/env python
# -*- coding: utf-8 -*-
 

import os
import requests
import datetime
import os.path
import datetime
import smtplib

yuyyuyu

import scipy.stats
import scipy.spatial
from numpy.random import RandomState
import matplotlib.pyplot as plt



rv=RandomState(123456789)
locations=rv.randint(0,511,size=(2,128))

yuyyuyu

import os
from scipy.io import wavfile
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from keras.layers import Conv2D, MaxPool2D, Flatten, LSTM
from keras.layers import Dropout, Dense, TimeDistributed
from keras.models import Sequential
from keras.utils import to_categorical
from sklearn.utils.class_weight import compute_class_weight

yuyyuyu

import os
from scipy.io import wavfile
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from keras.layers import Conv2D, MaxPool2D, Flatten, LSTM
from keras.layers import Dropout, Dense, TimeDistributed
from keras.models import load_model
from keras.utils import to_categorical
from sklearn.utils.class_weight import compute_class_weight