Numpy Structure and Efficiency

[garynewport]

I have the following code:

import matplotlib.pyplot as plt
import matplotlib.cbook as cbook

import numpy as np
import math

SIGMA = 5.67051e-5														# Stefan-Boltzmann Constant
PI    = math.pi															# Pi
M_SUN = 1.989e33														# Solar mass (g)
L_SUN = 3.826e33														# Solar luminosity (ergs/s)
R_SUN = 6.9599e10														# Solar radius (cm)
YEAR  = 3.15570e7														# AU year (s)


sa_sun = 4 * PI * math.pow(R_SUN, 2)									# Solar surface area
t_sun  = L_SUN / (sa_sun * SIGMA)										# Solar surface temperature



# Converts Fortran output (xd+/-y) into float
def convert(oldValue):
	if oldValue.find('D') != -1:
		newValue = float(oldValue.replace('D', 'e'))
	else:
		if oldValue.find('+') != -1:
			mantissa = float(oldValue[0: oldValue.find('+')])
			exponent = int(oldValue[oldValue.find('+'):])
			
		else:
			mantissa = float(oldValue[0: oldValue.find('-')])
			exponent = int(oldValue[oldValue.find('-'):])

		newValue = mantissa * (math.pow(10, exponent))
	
	return newValue



# Reads in data from STELCOR file
def readData(datafile):
	
	data = np.array([])
	
	file = open(datafile)

	for line in file:
		if (line[1:6] == "hydro") or (line[1:6] == "HYDRO"):
			time      = convert(line[21:35])
			delta_t	  = convert(line[36:50])
			mass      = convert(line[51:65])
			radius    = convert(line[66:80])
			lum_core  = convert(line[81:95])
			lum_tot   = convert(line[96:110])
			flux      = convert(line[111:125])
			ratio     = float(line[125:137])
			data = np.append(data, [time, delta_t, mass, radius, lum_core, lum_tot, flux, ratio], axis = 0)
			
	file.close()
	
	return data



if __name__ == "__main__":
	stellarData = np.array([])

	stellarData = readData("dummymain.lst")
	#CM_Graph(?,?)

I am adding the data from a textfile that contains thousands of lines, similar to this:

Output:
hydro output:     1    1.05200D+09    1.05200D+09    9.94376D+31    3.66754D+10    7.52265D+31    7.52265D+31    4.99722-235   0.0499938
 hydro output:     2    2.10400D+09    1.05200D+09    9.94376D+31    3.66754D+10    7.52265D+31    7.52265D+31    2.88583-105   0.0499938
 hydro output:     3    3.15600D+09    1.05200D+09    9.94376D+31    3.66754D+10    7.52265D+31    7.52265D+31    3.81557D-62   0.0499938
 hydro output:     4    4.20800D+09    1.05200D+09    9.94376D+31    3.66754D+10    7.52265D+31    7.52265D+31    1.19575D-40   0.0499938
 hydro output:     5    5.26000D+09    1.05200D+09    9.94376D+31    3.66754D+10    7.52265D+31    7.52265D+31    8.64733D-28   0.0499938
 hydro output:     6    6.31200D+09    1.05200D+09    9.94376D+31    3.66754D+10    7.52265D+31    7.52265D+31    3.04933D-19   0.0499938
 hydro output:     7    7.36400D+09    1.05200D+09    9.94376D+31    3.66754D+10    7.52265D+31    7.52265D+31    3.72610D-13   0.0499938
 hydro output:     8    8.41600D+09    1.05200D+09    9.94376D+31    3.66754D+10    7.52265D+31    7.52265D+31    1.32716D-08   0.0499938
 hydro output:     9    9.46800D+09    1.05200D+09    9.94376D+31    3.66754D+10    7.52265D+31    7.52265D+31    4.49291D-05   0.0499938
 hydro output:    10    1.05200D+10    1.05200D+09    9.94376D+31    3.66754D+10    7.52265D+31    7.52265D+31    2.93595D-02   0.0499938

The system strips out the initial text, ignores the incremental step (integer) value and populates an array with the converted float data.

All the data is added to a Numpy array of 1 dimension.

I originally envisaged a structure such as:

Output:
[[1052000000.0, 1052000000.0, 9.94376e+31, 36675400000.0, 7.52265e+31, 7.52265e+31, 4.9972200000000005e-235, 0.0499938], [...], ...]

However, I get these all as a single element in each array position@

Output:
[1052000000.0, 1052000000.0, 9.94376e+31, 36675400000.0, 7.52265e+31, 7.52265e+31, 4.9972200000000005e-235, 0.0499938]

I later want to reference all rows of data, but only the data in a specific position in that row (all data in column 3, for example).

How might I alter this and is there a more efficient way of collecting/storing the data?