if(__name__ == "__main__"):
    print 'It is a main programm module-file'
else:
    print 'It is just a module'

import math
print 'PI number is %.10f' % math.pi # formatted output

import math # without using short name
print math.pi

import math as m # using short name
print m.pi

import math # without using short name
print math.pi

from math import pi, cos # using from 
print pi
print 'cos: %f' % cos(0.0) # formatted output

from math import pi as pin # using from & as
print pin


from math import *
print sin(1.0)
print pi

from scipy import stats

def trend(x,y,out='spv'):

    '''
    From given vectors 'x' and 'y' linear trend components are calculated \n

    **INPUT:** \n
           `x [np.ma.arr]` - numpy 1D array of values \n
           `y [np.ma.arr]` - numpy masked 1D array of values \n
           `char [str]` - defines the calculated parameter (sum, mean \
           and so on)(default='mean') \n
           `anom [bool]` - switch array values into anomalies divided on std \
           (default=False) \n
    **OUTPUT:** \n
              `slope [float]` - slope koef. \n
              `p_value [float]` - p_value \n
              `sig [float]` - shows alpha-level, where trend is \
              statistically significant  \n
    
    Author: Pavel Shabanov \n
    Email: meteomail@yandex.ru \n
    Blog: http://geofortran.blogspot.ru \n
        
    ''' 
    slope, intercept, r_value, p_value, std_err = stats.mstats.linregress(x,y)

    ss = np.arange(1.0,0.00,-0.01)
    for i in ss:
#        print p_value, i
        if (p_value < i): 
            sig = i

    if (out == 'all'):
        return slope, intercept, r_value, p_value, std_err
    elif (out == 'ab'):
        return slope, intercept
    elif (out == 'spv'):
        return slope, p_value
    elif (out == 'pv'):
        return p_value
    elif (out == 's'):
        return slope
    elif (out == 'spvr'):
        return slope, p_value, r_value**2        
    else:
        return slope, p_value, sig


import scipy.stats.mstats.linregress as linetr

import numpy as np

import scimod as sm

x1 = np.arange(10)
y1 = np.random.random(10)
print x1,y1
a,b = sm.trend(x1,y1,out='ab')
print 'A: %f | B: %f' % (a,b)

import matplotlib.pyplot as plt
import numpy as np

def graph2d(x, y, color='black',legend=False):
    '''
    Это функция рисует двумерный график по двум входящим массивам.
    **INPUT:**
        `x [np.array] - argument, time`
        `y [np.array] - function`
        `color [str] - graph color (Default: 'black')`
        `legend [bool] - print or not graph legend (Default: False)`
    **OUTPUT:**
        `picture` - 2D graph
    '''
    
    xy = plt.plot(x,y,color,label='Random')
#    xy2 = plt.plot(x,y-0.5,color)    
    plt.grid(True)
    plt.ylabel('Y')
    plt.xlabel('Time')
    plt.xlim(x[0],x[-1])
    if(legend):
        plt.legend(frameon=False, loc='best')
   
    plt.show()

# ----- PROGRAM BODY -----    
# Testing our module

N = 10
x1 = np.arange(N)
y1 = np.random.random(N)
pic = graph2d(x1,y1, color='red',legend=True)
print 'Описание функции graph2d:', graph2d.__doc__