In [3]:

    

# !wget -O land_global_temperature_anomaly-1880-2015.csv https://www.ncdc.noaa.gov/cag/time-series/global/globe/land/all/1/1880-2015.csv


    

In [18]:

    

import numpy


    

In [19]:

    

!head land_global_temperature_annomaly-1880-2015.csv


    

    




Global Land Temperature Anomalies
Units: Degrees Celsius
Base Period: 1901-2000
Year,Value
188001,-0.1538
188002,-0.5771
188003,-0.7250
188004,-0.2990
188005,-0.2098
188006,-0.3002

In [21]:

    

T_Lan = numpy.loadtxt(fname='land_global_temperature_annomaly-1880-2015.csv',delimiter=',',skiprows=4)


    

In [22]:

    

print(T_Lan)


    

    




[[  1.88001000e+05  -1.53800000e-01]
 [  1.88002000e+05  -5.77100000e-01]
 [  1.88003000e+05  -7.25000000e-01]
 ..., 
 [  2.01510000e+05   1.32120000e+00]
 [  2.01511000e+05   1.28280000e+00]
 [  2.01512000e+05   1.87220000e+00]]

In [23]:

    

T_Lan.shape


    

    
Out[23]:





(1632, 2)

In [26]:

    

from matplotlib import pyplot
%matplotlib inline


    

In [27]:

    

T_Lan[0]


    

    
Out[27]:





array([  1.88001000e+05,  -1.53800000e-01])

In [34]:

    

T_Lan[:,1]


    

    
Out[34]:





array([-0.1538, -0.5771, -0.725 , ...,  1.3212,  1.2828,  1.8722])

In [38]:

    

# You can delet the uglu line by adding ';' at the end of the plot command
pyplot.plot(T_Lan[:,1]);


    

In [40]:

    

pyplot.plot(T_Lan[:,0],T_Lan[:,1]);


    

In [41]:

    

pyplot.plot(T_Lan[0:30,0],T_Lan[0:30,1]);


    

In [45]:

    

date = numpy.arange('1880','2016',dtype=('datetime64[M]'))


    

In [46]:

    

print(date)


    

    




['1880-01' '1880-02' '1880-03' ..., '2015-10' '2015-11' '2015-12']

In [53]:

    

dummy = numpy.linspace(1,1632,1632)

print(dummy)


    

    




[  1.00000000e+00   2.00000000e+00   3.00000000e+00 ...,   1.63000000e+03
   1.63100000e+03   1.63200000e+03]

In [60]:

    

# too many labels in the x axis.
#pyplot.xticks(dummy,date rotation=75);

pyplot.xticks(dummy[::12*10],date[::12*10], rotation=75);

pyplot.plot(dummy,T_Lan[:,1]);


    

In [63]:

    

from matplotlib import rcParams
rcParams['font.family'] = 'serif'
rcParams['font.size'] = 16


    

In [105]:

    

pyplot.figure(figsize=(10,6))

pyplot.xticks(dummy[::12*10],date[::12*10], rotation=40)

pyplot.plot(dummy, T_Lan[:,1], color='#2380a3' , linestyle='-', linewidth =1)

pyplot.title('Land global temperature anomalies.\n')

pyplot.xlabel('Year-month')
pyplot.ylabel('Land temperature anomaly [°C]')
pyplot.grid()

# CHECK why it is croping the plot.
# pyplot.savefig('temp_anomalies.png')


    

In [107]:

    

pyplot.figure(figsize=(10,5))
pyplot.hist(T_Lan[:,1])

pyplot.xlabel('Land temperature anomaly [°C]')
pyplot.ylabel('Frequency');


    

In [143]:

    

pyplot.figure(figsize=(10,5))
pyplot.hist(T_Lan[:,1], bins=30, normed = True, color='g', alpha=0.6)

pyplot.xlabel('Land temperature anomaly [°C]')
pyplot.ylabel('Normalized Frequency');


    

In [138]:

    

mean_T = numpy.mean(T_Lan[:,1])

median_T = numpy.median(T_Lan[:,1])

print('The mean value is {:.5}\nThe median value is {:.5}'.format(mean_T,median_T))


    

    




The mean value is 0.04031
The median value is -0.0276

In [137]:

    

variance_T = numpy.var(T_Lan[:,1])
sigma_T = numpy.sqrt(variance_T)

print('The variance is {:.5}'.format(variance_T))
print('The standard deviation is {:.5}'.format(sigma_T))


    

    




The variance is 0.28041
The standard deviation is 0.52954

In [139]:

    

from scipy import stats


    

In [148]:

    

bins = numpy.linspace(min(T_Lan[:,1]),max(T_Lan[:,1]),40)

pyplot.figure(figsize=(10,5))

pyplot.hist(T_Lan[:,1], bins, normed = True, color='g', alpha=0.6)

#Plot the probability densitty function
pyplot.plot(bins, stats.norm.pdf(bins,mean_T,sigma_T), color='#ff5733', ls='-',lw=2)

pyplot.xlabel('Land temperature anomaly [°C]')
pyplot.ylabel('Normalized Frequency');


    

In [161]:

    

pyplot.figure(figsize=(15,5))

#First Plot
pyplot.subplot(121)
pyplot.xticks(dummy[::12*10],date[::12*10], rotation=40)
pyplot.plot(dummy, T_Lan[:,1], color='#2380a3' , linestyle='-', linewidth =1,alpha=1)
#pyplot.title('Land global temperature anomalies.\n')
pyplot.xlabel('Year-month')
pyplot.ylabel('Land temperature anomaly [°C]')
pyplot.grid();

#Second Plot
pyplot.subplot(122)
bins = numpy.linspace(min(T_Lan[:,1]),max(T_Lan[:,1]),40)
pyplot.hist(T_Lan[:,1], bins, normed = True, color='#2380a3', alpha=0.6,histtype='bar') #histtype: bar & stepfilled
#Plot the probability densitty function
pyplot.plot(bins, stats.norm.pdf(bins,mean_T,sigma_T), color='#ff5733', ls='-',lw=2.5)
pyplot.xlabel('Land temperature anomaly [°C]')
pyplot.ylabel('Normalized Frequency');


    

In [162]:

    

def smooth_data(N,data):
    """
    Returns smoothed data using a sliding_moving average
    
    Arguments:
    ----------
    N        (int): Amount of data values that will be averaged
    data   (array): Array of data we want to smooth.
    
    
    Returns:
    --------
    smooth (array): array with smothed data.
    """
    
    window = numpy.ones(N)/N
    smooth = numpy.convolve(data,window,'same')
    
    return smooth


    

In [164]:

    

# To check what a function does apply "?<function name>"

?smooth_data


    

In [169]:

    

smooth = smooth_data(12,T_Lan[:,1])


    

In [170]:

    

print(smooth)


    

    




[-0.18874167 -0.21688333 -0.24125833 ...,  0.93280833  0.843575    0.7415    ]

In [189]:

    

pyplot.figure(figsize=(10,5))

pyplot.xticks(dummy[::12*10],date[::12*10], rotation=40)

pyplot.plot(dummy, smooth, color='r' , linestyle='-', linewidth =2,alpha=1)

pyplot.title('Land global temperature anomalies averaged.\n')

pyplot.xlabel('Year-month')
pyplot.ylabel('Land temperature anomaly [°C]')
pyplot.grid()


    

In [192]:

    

pyplot.figure(figsize=(10,5))

pyplot.xticks(dummy[::12*10],date[::12*10], rotation=40)

pyplot.plot(dummy, T_Lan[:,1], color='g' , linestyle='-', linewidth =1, alpha=0.8)
pyplot.plot(dummy, smooth, color='r' , linestyle='-', linewidth =2,alpha=1)

pyplot.title('Land global temperature anomalies.\n(Data (g) and Smooth Data(r))\n')

pyplot.xlabel('Year-month')
pyplot.ylabel('Land temperature anomaly [°C]')
pyplot.grid()


    

In [194]:

    

m,b = numpy.polyfit(dummy,T_Lan[:,1],1)
print (m,b)


    

    




0.000832212749511 -0.639191783505

In [196]:

    

f_linear = numpy.poly1d((m,b))
print(f_linear)


    

    




 
0.0008322 x - 0.6392

In [201]:

    

pyplot.figure(figsize=(10,5))

pyplot.xticks(dummy[::12*10],date[::12*10], rotation=40)

#Original Data series
pyplot.plot(dummy, T_Lan[:,1], color='g' , linestyle='-', linewidth =1, alpha=0.8, label='original data')

#Linear Regression
pyplot.plot(dummy,f_linear(dummy), color='k', ls='--', linewidth=2, label='linear regression')


#Averagedfunction
#pyplot.plot(dummy, smooth, color='r' , linestyle='-', linewidth =2,alpha=1)

pyplot.title('Land global temperature annomalies\n')

pyplot.xlabel('Year-month')
pyplot.ylabel('Land temperature anomaly [°C]')
pyplot.legend()
pyplot.grid()