In [1]:

    

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


    

In [2]:

    

my_data = np.loadtxt('myGAMA_ALL_AB_ABSOL_MAGS.csv', delimiter=',', dtype=str)


    

In [3]:

    

print my_data.shape


    

    




(43969, 175)

In [4]:

    

my_dictionary = {}
for i in range(len(my_data[0, :])):                                         # Converting numpy array into dictionary
    my_dictionary[my_data[0, i]] = np.array(my_data[0 + 1:, i], dtype=str)


    

In [5]:

    

redshift = my_dictionary['Z_HELIO'].astype(float)
fuv_band = my_dictionary['MAG_AB_FUV'].astype(float)   
nuv_band = my_dictionary['MAG_AB_NUV'].astype(float)
r_band   = my_dictionary['MAG_AB_R'].astype(float)


    

In [6]:

    

print redshift[[fuv_band>0]].shape
print fuv_band[[fuv_band>0]].shape


    

    




(42610,)
(42610,)

In [7]:

    

print redshift.shape
print fuv_band.shape


    

    




(43968,)
(43968,)

In [8]:

    

print redshift.size
print fuv_band.size


    

    




43968
43968

In [9]:

    

indexes = np.arange(redshift.size)


    

In [10]:

    

index_clean = indexes[(redshift>0.015)*(r_band>0)*(nuv_band>0)*(fuv_band>0)*((fuv_band-nuv_band)<50)*((fuv_band-nuv_band)>(-20))]


    

In [11]:

    

print redshift[index_clean].size


    

    




41573

In [12]:

    

my_clean_data = my_data[index_clean].astype(str)


    

In [13]:

    

print my_clean_data.shape
#print my_clean_data[0,:]  #checking if the header is ok!


    

    




(41573, 175)

In [14]:

    

my_df = pd.DataFrame(my_clean_data)


    

In [15]:

    

my_df.to_csv('myGAMA_ALL_AB_ABSOL_MAGS_clean.csv', sep=',', header=None, index=False)


    

In [16]:

    

!pwd


    

    




/home/mldantas/Dropbox/DoutoradoIAG/GAMAZOO

In [ ]: