#its a RE
descriptions = myDB.get_filenames_with_re(args.get)
x_values = []; y_values = []
x_total_proc_values = []; y_total_proc_values = []
for d in descriptions:
values = myDB.get_original_data(d)
x_values += values[0]
y_values += values[1]
xps, yps = myDB.get_processed_data(d)
x_total_proc_values += xps
y_total_proc_values += yps
print x_values, y_values
#Now we can do something with this values...
if args.put:
if path.isfile(args.put):
myDB.insert_data(args.put)
else:
insert_multiple_files(args.put)
if args.get_lineal:
std, slope, intercept, r_value, p_value = myDB.get_lineal_regression(args.get_lineal)
#do something with this values
print """
std : %1.8f
slope : %1.8f
intercept : %1.8f
r_value : %1.8f
p_value : %1.8f """ % (std, slope, intercept, r_value, p_value)
pylab.title(title)
pylab.text(400, -0.0002, about, {'color': 'g', 'fontsize': 15})
pylab.xlabel('x')
pylab.ylabel('y')
pylab.savefig("../plots/matplot2std.png", dpi = 100)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='For test linealregression and standar desviation')
parser.add_argument("-f", "--file", dest="file", help='This option is used to pass the data file')
args = parser.parse_args()
myDB = Repositorio(MYDB)
#nos aseguramos de guardar en la db el archivo
values = myDB.insert_data(args.file)
#obtain de std of y values
desviation_y = do_std(values[1])
#dump into the db
myDB.dump_data(args.file, [None], [desviation_y], "std")
plot_data(values[1], r'$\sigma = %.18f $' %(desviation_y), "y_values and std")
#do the same with lineal regression
results_lineal = do_linealregression(values[0], values[1])
labels = ['slope', 'intercept', 'r_value', 'p_value']
for value, about in zip(results_lineal, labels):
myDB.dump_data(args.file, [None], [value], about)
filename = "../fileTest/liear_fit" + path.basename(args.file)
save_ascii(filename, labels, results_lineal)
